U.S. patent application number 15/017495 was filed with the patent office on 2016-08-11 for methods and systems for recommending agricultural activities.
The applicant listed for this patent is The Climate Corporation. Invention is credited to Sivan Aldor-Noiman, Erik Andrejko, Moorea Brega, Tristan D'Orgeval, James Ethington, Coco Krumme, Evin Levey, Cory Muhlbauer, Eli Pollak, Doug Sauder, Alex Wimbush.
Application Number | 20160232621 15/017495 |
Document ID | / |
Family ID | 55404851 |
Filed Date | 2016-08-11 |
United States Patent
Application |
20160232621 |
Kind Code |
A1 |
Ethington; James ; et
al. |
August 11, 2016 |
METHODS AND SYSTEMS FOR RECOMMENDING AGRICULTURAL ACTIVITIES
Abstract
A computer-implemented method for recommending agricultural
activities is implemented by an agricultural intelligence computer
system in communication with a memory. The method includes
receiving a plurality of field definition data, retrieving a
plurality of input data from a plurality of data networks,
determining a field region based on the field definition data,
identifying a subset of the plurality of input data associated with
the field region, determining a plurality of field condition data
based on the subset of the plurality of input data, identifying a
plurality of field activity options, determining a recommendation
score for each of the plurality of field activity options based at
least in part on the plurality of field condition data, and
providing a recommended field activity option from the plurality of
field activity options based on the plurality of recommendation
scores.
Inventors: |
Ethington; James; (San
Francisco, CA) ; Pollak; Eli; (San Francisco, CA)
; D'Orgeval; Tristan; (Paris, FR) ; Krumme;
Coco; (Berkeley, CA) ; Levey; Evin;
(Kentfield, CA) ; Wimbush; Alex; (San Francisco,
CA) ; Andrejko; Erik; (Oakland, CA) ; Brega;
Moorea; (San Francisco, CA) ; Aldor-Noiman;
Sivan; (Foster City, CA) ; Sauder; Doug;
(Livermore, CA) ; Muhlbauer; Cory; (Bloomington,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Climate Corporation |
San Francisco |
CA |
US |
|
|
Family ID: |
55404851 |
Appl. No.: |
15/017495 |
Filed: |
February 5, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62113229 |
Feb 6, 2015 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/02 20130101;
G06Q 10/06315 20130101; G06Q 10/04 20130101; G06Q 10/0637 20130101;
A01B 79/02 20130101; G06Q 10/06 20130101 |
International
Class: |
G06Q 50/02 20060101
G06Q050/02; G06Q 10/06 20060101 G06Q010/06 |
Claims
1. A computer-implemented method comprising: using a computing
device with a memory, receiving, over a network, field definition
data and field condition data for one or more fields; using the
computing device, computing, based on the field definition data and
field condition data for the one or more fields, a variable rate
sustainability score for the one or more fields, using the
computing device, causing displaying, on a client computing device,
an association of the variable rate sustainability score with the
one or more fields.
2. The computer-implemented method of claim 1, further comprising:
determining, from the field definition data and the field condition
data for the one or more fields, index values of biomass health for
a plurality of regions of the one or more fields; determining, from
the index values of biomass health, a statistical variation of
biomass health for the plurality of regions of the one or more
fields; computing the variable rate sustainability score based, at
least in part, on the statistical variation of biomass health.
3. The computer-implemented method of claim 1, further comprising:
determining, from the field definition data and the field condition
data for the one or more fields, elevation values of a plurality of
regions of the one or more fields; determining, from the elevation
values, a statistical variation of elevation for the plurality of
regions of the one or more fields; computing the variable rate
sustainability score based, at least in part, on the statistical
variation off elevation.
4. The computer-implemented method of claim 1, further comprising:
determining, from the field definition data and the field condition
data for the one or more fields, a number of unique soil types in
the one or more fields; determining a distinctness of each unique
soil type of the number of unique soil types in the one or more
fields; computing the variable rate sustainability score based, at
least in part, on the number of unique soil types and the
distinctness of each unique soil type in the one or more
fields.
5. The computer-implemented method of claim 4, further comprising:
determining that the variable rate sustainability score exceeds a
threshold value; in response to the determining, generating
soil-based management zones based, at least in part, on the unique
soil types in the one or more fields; causing displaying, on the
client computing device, the soil-based management zones.
6. The computer-implemented method of claim 5, further comprising:
identifying, from the field definition data and field condition
data for the one or more fields, one or more secondary spatial
characteristics; modifying the soil-based management zones based on
the one or more secondary spatial characteristics; causing
displaying, on the client computing device, the modified soil-based
management zones.
7. The computer-implemented method of claim 6, wherein the one or
more secondary spatial characteristics comprise one or more of:
elevation, electrical conductivity, organic matter content,
presence of tilling, presence of irrigation, yield from one or more
prior seasons, reflectivity, characteristics derived from
reflectivity, or thermal emissivity.
8. One or more non-transitory computer readable media storing
instructions which, when executed by one or more processors, cause:
receiving, over a network, field definition data and field
condition data for one or more fields; computing, based on the
field definition data and field condition data for the one or more
fields, a variable rate sustainability score for the one or more
fields, causing displaying, on a client computing device, an
association of the variable rate sustainability score with the one
or more fields.
9. The one or more non-transitory computer readable media of claim
8 wherein the instructions, when executed by the one or more
processors, further cause: determining, from the field definition
data and the field condition data for the one or more fields, index
values of biomass health for a plurality of regions of the one or
more fields; determining, from the index values of biomass health,
a statistical variation of biomass health for the plurality of
regions of the one or more fields; computing the variable rate
sustainability score based, at least in part, on the statistical
variation of biomass health.
10. The one or more non-transitory computer readable media of claim
8 wherein the instructions, when executed by the one or more
processors, further cause: determining, from the field definition
data and the field condition data for the one or more fields,
elevation values of a plurality of regions of the one or more
fields; determining, from the elevation values, a statistical
variation of elevation for the plurality of regions of the one or
more fields; computing the variable rate sustainability score
based, at least in part, on the statistical variation off
elevation.
11. The one or more non-transitory computer readable media of claim
8 wherein the instructions, when executed by the one or more
processors, further cause: determining, from the field definition
data and the field condition data for the one or more fields, a
number of unique soil types in the one or more fields; determining
a distinctness of each unique soil type of the number of unique
soil types in the one or more fields; computing the variable rate
sustainability score based, at least in part, on the number of
unique soil types and the distinctness of each unique soil type in
the one or more fields.
12. The one or more non-transitory computer readable media of claim
11 wherein the instructions, when executed by the one or more
processors, further cause: determining that the variable rate
sustainability score exceeds a threshold value; in response to the
determining, generating soil-based management zones based, at least
in part, on the unique soil types in the one or more fields;
causing displaying, on the client computing device, the soil-based
management zones.
13. The one or more non-transitory computer readable media of claim
12 wherein the instructions, when executed by the one or more
processors, further cause: identifying, from the field definition
data and field condition data for the one or more fields, one or
more secondary spatial characteristics; modifying the soil-based
management zones based on the one or more secondary spatial
characteristics; causing displaying, on the client computing
device, the modified soil-based management zones.
14. The one or more non-transitory computer readable media of claim
13, wherein the one or more secondary spatial characteristics
comprise one or more of: elevation, electrical conductivity,
organic matter content, presence of tilling, presence of
irrigation, yield from one or more prior seasons, reflectivity,
characteristics derived from reflectivity, or thermal
emissivity.
15. A computer system comprising: one or more processors and one or
more memories communicatively coupled to the one or more
processors, said processors configured to: receive, over a network,
field definition data and field condition data for one or more
fields; compute, based on the field definition data and field
condition data for the one or more fields, a variable rate
sustainability score for the one or more fields, cause displaying,
on a client computing device, an association of the variable rate
sustainability score with the one or more fields.
16. The system of claim 15, wherein the one or more processors are
further configured to: determine, from the field definition data
and the field condition data for the one or more fields, index
values of biomass health for a plurality of regions of the one or
more fields; determine, from the index values of biomass health, a
statistical variation of biomass health for the plurality of
regions of the one or more fields; compute the variable rate
sustainability score based, at least in part, on the statistical
variation of biomass health.
17. The system of claim 15, wherein the one or more processors are
further configured to: determine, from the field definition data
and the field condition data for the one or more fields, elevation
values of a plurality of regions of the one or more fields;
determine, from the elevation values, a statistical variation of
elevation for the plurality of regions of the one or more fields;
compute the variable rate sustainability score based, at least in
part, on the statistical variation off elevation.
18. The system of claim 15, wherein the one or more processors are
further configured to: determine, from the field definition data
and the field condition data for the one or more fields, a number
of unique soil types in the one or more fields; determine a
distinctness of each unique soil type of the number of unique soil
types in the one or more fields; compute the variable rate
sustainability score based, at least in part, on the number of
unique soil types and the distinctness of each unique soil type in
the one or more fields.
19. The system of claim 18, wherein the one or more processors are
further configured to: determine that the variable rate
sustainability score exceeds a threshold value; in response to the
determining, generate soil-based management zones based, at least
in part, on the unique soil types in the one or more fields; cause
displaying, on the client computing device, the soil-based
management zones.
20. The system of claim 19, wherein the one or more processors are
further configured to: identify, from the field definition data and
field condition data for the one or more fields, one or more
secondary spatial characteristics; modify the soil-based management
zones based on the one or more secondary spatial characteristics;
cause displaying, on the client computing device, the modified
soil-based management zones.
Description
BENEFIT CLAIM
[0001] This application claims the benefit under 35 U.S.C.
.sctn.119(e) of provisional application 62/113,229, filed Feb. 6,
2015, the entire contents of which is hereby incorporated by
reference as if fully set forth herein.
BACKGROUND
[0002] The embodiments described herein relate generally to
agricultural activities and, more particularly, systems and methods
for managing and recommending agricultural activities at the field
level based on crop-related data and field-condition data.
[0003] Agricultural production requires significant strategy and
analysis. In many cases, agricultural growers (e.g., farmers or
others involved in agricultural cultivation) are required to
analyze a variety of data to make strategic decisions months in
advance of the period of crop cultivation (i.e., growing season).
In making such strategic decisions, growers must consider at least
some of the following decision constraints: fuel and resource
costs, historical and projected weather trends, soil conditions,
projected risks posed by pests, disease and weather events, and
projected market values of agricultural commodities (i.e., crops).
Analyzing these decision constraints may help a grower to predict
key agricultural outcomes including crop yield, energy usage, cost
and resource utilization, and farm profitability. Such analysis may
inform a grower's strategic decisions of determining crop
cultivation types, methods, and timing.
[0004] Despite its importance, such analysis and strategy is
difficult to accomplish for a variety of reasons. First, obtaining
reliable information for the various considerations of the grower
is often difficult. Second, aggregating such information into a
usable manner is a time consuming task. Third, where data is
available, it may not be precise enough to be useful to determine
strategy. For example, weather data (historical or projected) is
often generalized for a large region such as a county or a state.
In reality, weather may vary significantly at a much more granular
level, such as an individual field. In addition, terrain features
may cause weather data to vary significantly in even small
regions.
[0005] Additionally, growers often must regularly make decisions
during growing season. Such decisions may include adjusting when to
harvest, providing supplemental fertilizer, and how to mitigate
risks posed by pests, disease and weather. As a result, growers
must continually monitor various aspects of their crops during the
growing season including weather, soil, and crop conditions.
Accurately monitoring all such aspects at a granular level is
difficult and time consuming. Accordingly, methods and systems for
analyzing crop-related data and providing field condition data and
strategic recommendations for maximizing crop yield are
desirable.
BRIEF DESCRIPTION OF THE DISCLOSURE
[0006] In one aspect, a computer-implemented method for
recommending agricultural activities is provided. The method is
implemented by an agricultural intelligence computer system in
communication with a memory. The method includes receiving a
plurality of field definition data, retrieving a plurality of input
data from a plurality of data networks, determining a field region
based on the field definition data, identifying a subset of the
plurality of input data associated with the field region,
determining a plurality of field condition data based on the subset
of the plurality of input data, identifying a plurality of field
activity options, determining a recommendation score for each of
the plurality of field activity options based at least in part on
the plurality of field condition data, and providing a recommended
field activity option from the plurality of field activity options
based on the plurality of recommendation scores.
[0007] In another aspect, a networked agricultural intelligence
system for recommending agricultural activities is provided. The
networked agricultural intelligence system includes a user device,
a plurality of data networks computer systems, an agricultural
intelligence computer system comprising a processor and a memory in
communication with the processor. The processor is configured to
receive a plurality of field definition data from the user device,
retrieve a plurality of input data from a plurality of data
networks, determine a field region based on the field definition
data, identify a subset of the plurality of input data associated
with the field region, determine a plurality of field condition
data based on the subset of the plurality of input data, identify a
plurality of field activity options, determine a recommendation
score for each of the plurality of field activity options based at
least in part on the plurality of field condition data, and provide
a recommended field activity option from the plurality of field
activity options based on the plurality of recommendation
scores.
[0008] In a further aspect, computer-readable storage media for
recommending agricultural activities is provided. The
computer-readable storage media has computer-executable
instructions embodied thereon. When executed by at least one
processor, the computer-executable instructions cause a processor
to receive a plurality of field definition data from the user
device, retrieve a plurality of input data from a plurality of data
networks, determine a field region based on the field definition
data, identify a subset of the plurality of input data associated
with the field region, determine a plurality of field condition
data based on the subset of the plurality of input data, identify a
plurality of field activity options, determine a recommendation
score for each of the plurality of field activity options based at
least in part on the plurality of field condition data, and provide
a recommended field activity option from the plurality of field
activity options based on the plurality of recommendation
scores.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a diagram depicting an example agricultural
environment including a plurality of fields that are monitored and
managed with an agricultural intelligence computer system that is
used to manage and recommend agricultural activities;
[0010] FIG. 2 is a block diagram of a user computing device, used
for managing and recommending agricultural activities, as shown in
the agricultural environment of FIG. 1;
[0011] FIG. 3 is a block diagram of a computing device, used for
managing and recommending agricultural activities, as shown in the
agricultural environment of FIG. 1;
[0012] FIG. 4 is an example data flowchart of managing and
recommending agricultural activities using the computing devices of
FIGS. 1, 2, and 3 in the agricultural environment shown in FIG.
1;
[0013] FIG. 5 is an example method for managing agricultural
activities in the agricultural environment of FIG. 1;
[0014] FIG. 6 is an example method for recommending agricultural
activities in the agricultural environment of FIG. 1;
[0015] FIG. 7 is a diagram of an example computing device used in
the agricultural environment of FIG. 1 to recommend and manage
agricultural activities; and
[0016] FIGS. 8-30 are example illustrations of information provided
by the agricultural intelligence computer system of FIG. 3 to the
user device of FIG. 2 to facilitate the management and
recommendation of agricultural activities.
[0017] FIG. 31 is an example method for determining variable rate
suitability.
[0018] FIG. 32 is an example method for recommending yield
management zones.
[0019] FIG. 33 illustrates an exemplary graph of microorganism
growth.
[0020] FIG. 34 is an example method for recommending starter
application.
[0021] FIG. 35 illustrates exemplary maps of elevation and yield
loss.
[0022] FIG. 36 is an example method for generating a water
management recommendation.
[0023] FIG. 37 is an example method for determining a water
management economic loss.
[0024] Although specific features of various embodiments may be
shown in some drawings and not in others, this is for convenience
only. Any feature of any drawing may be referenced and/or claimed
in combination with any feature of any other drawing.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0025] The following detailed description of the embodiments of the
disclosure refers to the accompanying drawings. The same reference
numbers in different drawings may identify the same or similar
elements. Also, the following detailed description does not limit
the claims.
[0026] The subject matter described herein relates generally to
managing and recommending agricultural activities for a user such
as a grower or a farmer. Specifically, a first embodiment of the
methods and systems described herein includes (i) receiving a
plurality of field definition data, (ii) retrieving a plurality of
input data from a plurality of data networks, (iii) determining a
field region based on the field definition data, (iv) identifying a
subset of the plurality of input data associated with the field
region, (v) determining a plurality of field condition data based
on the subset of the plurality of input data, and (vi) providing
the plurality of field condition data to the user device.
[0027] A second embodiment of the methods and systems described
herein includes (i) receiving a plurality of field definition data,
(ii) retrieving a plurality of input data from a plurality of data
networks, (iii) determining a field region based on the field
definition data, (iv) identifying a subset of the plurality of
input data associated with the field region, (v) determining a
plurality of field condition data based on the subset of the
plurality of input data, (vi) identifying a plurality of field
activity options, (vii) determining a recommendation score for each
of the plurality of field activity options based at least in part
on the plurality of field condition data, and (viii) providing a
recommended field activity option from the plurality of field
activity options based on the plurality of recommendation
scores.
[0028] In at least some agricultural environments (e.g., farms,
groups of farms, and other agricultural cultivation environments),
agricultural growers employ significant strategy and analysis to
make decisions on agricultural cultivation. In many cases, growers
analyze a variety of data to make strategic decisions months in
advance of the period of crop cultivation (i.e., growing season).
In making such strategic decisions, growers must consider at least
some of the following decision constraints: fuel and resource
costs, historical and projected weather trends, soil conditions,
projected risks posed by pests, disease and weather events, and
projected market values of agricultural commodities (i.e., crops).
Analyzing these decision constraints may help a grower to predict
key agricultural outcomes including crop yield, energy usage, cost
and resource utilization, and farm profitability. Such analysis may
inform a grower's strategic decisions of determining crop
cultivation types, methods, and timing. Despite its importance,
such analysis and strategy is difficult to accomplish for a variety
of reasons. First, obtaining reliable information for the various
considerations of the grower is often difficult. Second,
aggregating such information into a usable manner is a time
consuming task. Third, where data is available, it may not be
precise enough to be useful to determine strategy. For example,
weather data (historical or projected) is often generalized for a
large region such as a county or a state. In reality, weather may
vary significantly at a much more granular level, such as an
individual field. Terrain features may cause weather data to vary
significantly in even small regions.
[0029] Additionally, growers often must regularly make decisions
during growing season. Such decisions may include adjusting when to
harvest, providing supplemental fertilizer, and how to mitigate
risks posed by pests, disease and weather. As a result, growers
must continually monitor various aspects of their crops during the
growing season including weather, soil, and crop conditions.
Accurately monitoring all such aspects at a granular level is
difficult and time consuming. Accordingly, methods and systems for
analyzing crop-related data, and providing field condition data and
strategic recommendations for maximizing crop yield are desirable.
Accordingly, the systems and methods described herein facilitate
the management and recommendation of agricultural activities to
growers.
[0030] As used herein, the term "agricultural intelligence
services" refers to a plurality of data providers used to aid a
user (e.g., a farmer, agronomist or consultant) in managing
agricultural services and to provide the user with recommendations
of agricultural services. As used herein, the terms "agricultural
intelligence service", "data network", "data service", "data
provider", and "data source" are used interchangeably herein unless
otherwise specified. In some embodiments, the agricultural
intelligence service may be an external data network (e.g., a
third-party system). As used herein, data provided by any such
"agricultural intelligence services" or "data networks" may be
referred to as "input data", or "source data."
[0031] As used herein, the term "agricultural intelligence computer
system" refers to a computer system configured to carry out the
methods described herein. The agricultural intelligence computer
system is in networked connectivity with a "user device" (e.g.,
desktop computer, laptop computer, smartphone, personal digital
assistant, tablet or other computing device) and a plurality of
data sources. In the example embodiment, the agricultural
intelligence computer system provides the agricultural intelligence
services using a cloud-based software as a service (SaaS) model.
Therefore, the agricultural intelligence computer system may be
implemented using a variety of distinct computing devices. The user
device may interact with the agricultural intelligence computer
system using any suitable network.
[0032] In an example embodiment, an agricultural machine (e.g.,
combine, tractor, cultivator, plow, subsoiler, sprayer or other
machinery used on a farm to help with farming) may be coupled to a
computing device ("agricultural machine computing device") that
interacts with the agricultural intelligence computer system in a
similar manner as the user device. In some examples, the
agricultural machine computing device could be a planter monitor,
planter controller or a yield monitor. The agricultural machine and
agricultural machine computing device may provide the agricultural
intelligence computer system with field definition data and
field-specific data.
[0033] The term "field definition data" refers to field
identifiers, geographic identifiers, boundary identifiers, crop
identifiers, and any other suitable data that may be used to
identify farm land, such as a common land unit (CLU), lot and block
number, a parcel number, geographic coordinates and boundaries,
Farm Serial Number (FSN), farm number, tract number, field number,
section, township, and/or range. According to the United States
Department of Agriculture (USDA) Farm Service Agency, a CLU is the
smallest unit of land that has a permanent, contiguous boundary, a
common land cover and land management, a common owner and a common
producer in agricultural land associated with USDA farm programs.
CLU boundaries are delineated from relatively permanent features
such as fence lines, roads, and/or waterways. The USDA Farm Service
Agency maintains a Geographic Information Systems (GIS) database
containing CLUs for farms in the United States.
[0034] When field definition and field-specific data is not
provided directly to the agricultural intelligence computer system
via one or more agricultural machines or agricultural machine
devices that interacts with the agricultural intelligence computer
system, the user may be prompted via one or more user interfaces on
the user device (served by the agricultural intelligence computer
system) to input such information. In an example embodiment, the
user may identify field definition data by accessing a map on the
user device (served by the agricultural intelligence computer
system) and selecting specific CLUs that have been graphically
shown on the map. In an alternative embodiment, the user may
identify field definition data by accessing a map on the user
device (served by the agricultural intelligence computer system)
and drawing boundaries of the field over the map. Such CLU
selection or map drawings represent geographic identifiers. In
alternative embodiments, the user may identify field definition
data by accessing field definition data (provided as shape files or
in a similar format) from the U.S. Department of Agriculture Farm
Service Agency or other source via the user device and providing
such field definition data to the agricultural intelligence
computer system. The land identified by "field definition data" may
be referred to as a "field" or "land tract." As used herein, the
land farmed, or "land tract", is contained in a region that may be
referred to as a "field region." Such a "field region" may be
coextensive with, for example, temperature grids or precipitation
grids, as used and defined below.
[0035] The term "field-specific data" refers to (a) field data
(e.g., field name, soil type, acreage, tilling status, irrigation
status), (b) harvest data (e.g., crop type, crop variety, crop
rotation, whether the crop is grown organically, harvest date,
Actual Production History (APH), expected yield, yield, crop price,
crop revenue, grain moisture, tillage practice, weather information
(e.g., temperature, rainfall) to the extent maintained or
accessible by the user, previous growing season information), (c)
soil composition (e.g., pH, organic matter (OM), cation exchange
capacity (CEC)), (d) planting data (e.g., planting date, seed(s)
type, relative maturity (RM) of planted seed(s), seed population),
(e) nitrogen data (e.g., application date, amount, source), (f)
pesticide data (e.g., pesticide, herbicide, fungicide, other
substance or mixture of substances intended for use as a plant
regulator, defoliant, or desiccant), (g) irrigation data (e.g.,
application date, amount, source), and (h) scouting observations
(photos, videos, free form notes, voice recordings, voice
transcriptions, weather conditions (temperature, precipitation
(current and over time), soil moisture, crop growth stage, wind
velocity, relative humidity, dew point, black layer)). If
field-specific data is not provided via one or more agricultural
machines or agricultural machine devices that interacts with the
agricultural intelligence computer system in a similar manner as
the user device, a user may provide such data via the user device
to the agricultural intelligence computer system. In other words,
the user accesses the agricultural intelligence computer system via
the user device and provides the field-specific data.
[0036] The agricultural intelligence computer system also utilizes
environmental data to provide agricultural intelligence services.
The term "environmental data" refers to environmental information
related to farming activities such as weather information,
vegetation and crop growth information, seed information, pest and
disease information and soil information. Environmental data may be
obtained from external data sources accessible by the agricultural
intelligence computer system. Environmental data may also be
obtained from internal data sources integrated within the
agricultural intelligence computer system. Data sources for
environmental data may include weather radar sources,
satellite-based precipitation sources, meteorological data sources
(e.g., weather stations), satellite imagery sources, aerial imagery
sources (e.g., airplanes, unmanned aerial vehicles), terrestrial
imagery sources (e.g., agricultural machine, unmanned terrestrial
vehicle), soil sources and databases, seed databases, crop
phenology sources and databases, and pest and disease reporting and
prediction sources and databases. For example, a soil database may
relate soil types and soil locations to soil data including pH
levels, organic matter makeups, and cation exchange capacities.
Although in many examples, the user may access data from data
sources indirectly via the agricultural intelligence computer
system, in other examples, the user may directly access the data
sources via any suitable network connection.
[0037] The agricultural intelligence computer system processes the
plurality of field definition data, field-specific data and
environmental data from a plurality of data sources to provide a
user with the plurality of field condition data for the field or
field region identified by the field definition data. The term
"field condition data" refers to characteristics and conditions of
a field that may be used by the agricultural intelligence computer
system to manage and recommend agricultural activities. Field
condition data may include, for example, and without limitation,
field weather conditions, field workability conditions, growth
stage conditions, soil moisture, and precipitation conditions.
Field condition data is presented to the user using the user
device.
[0038] The agricultural intelligence computer system also provides
a user with a plurality of agricultural intelligence services for
the land tract or field region identified by the field definition
data. Such agricultural intelligence services may be used to
recommend courses of action for the user to undertake. In an
example embodiment, the recommendation services include a planting
advisor, a nitrogen application advisor, a pest advisor, a field
health advisor, a harvest advisor, and a revenue advisor. Each is
discussed herein.
[0039] System Architecture
[0040] As noted above, the agricultural intelligence computer
system may be implemented using a variety of distinct computing
devices using any suitable network. In an example embodiment, the
agricultural intelligence computer system uses a client-server
architecture configured for exchanging data over a network (e.g.,
the Internet). One or more user devices may communicate via a
network with a user application or an application platform. The
application platform represents an application available on user
devices that may be used to communicate with agricultural
intelligence computer system. Other example embodiments may include
other network architectures, such as peer-to-peer or distributed
network environment.
[0041] The application platform may provide server-side
functionality, via the network to one or more user devices.
Accordingly, the application platform may include client side
software stored locally at the user device as well as server side
software stored at the agricultural intelligence computer system.
In an example embodiment, the user device may access the
application platform via a web client or a programmatic client. The
user device may transmit data to, and receive data from one or more
front-end servers. In an example embodiment, the data may take the
form of requests and user information input, such as field-specific
data, into the user device. One or more front-end servers may
process the user device requests and user information and determine
whether the requests are service requests or content requests,
among other things. Content requests may be transmitted to one or
more content management servers for processing. Application
requests may be transmitted to one or more application servers. In
an example embodiment, application requests may take the form of a
request to provide field condition data and/or agricultural
intelligence services for one or more fields.
[0042] In an example embodiment, the application platform may
include one or more servers in communication with each other. For
example, the agricultural intelligence computer system may include
front-end servers, application servers, content management servers,
account servers, modeling servers, environmental data servers, and
corresponding databases. As noted above, environmental data may be
obtained from external data sources accessible by the agricultural
intelligence computer system or it may be obtained from internal
data sources integrated within the agricultural intelligence
computer system.
[0043] In an example embodiment, external data sources may include
third-party hosted servers that provide services to the
agricultural intelligence computer system via Application Program
Interface (API) requests and responses. The frequency at which the
agricultural intelligence computer system may consume data
published or made available by these third-party hosted servers may
vary based on the type of data. In an example embodiment, a
notification may be sent to the agricultural intelligence computer
system when new data is available by a data source. The
agricultural intelligence computer system may transmit an API call
via the network to the agricultural intelligence computer system
hosting the data and receive the new data in response to the call.
To the extent needed, the agricultural intelligence computer system
may process the data to enable components of the application
platform to handle the data. For example, processing data may
involve extracting data from a stream or a data feed and mapping
the data to a data structure, such as an XML data structure. Data
received and/or processed by the agricultural intelligence computer
system may be transmitted to the application platform and stored in
an appropriate database.
[0044] When an application request is made, the one or more
application servers communicate with the content management
servers, account servers, modeling servers, environmental data
servers, and corresponding databases. In one example, modeling
servers may generate a predetermined number of simulations (e.g.,
10,000 simulations) using, in part, field-specific data and
environmental data for one or more fields identified based on field
definition data and user information. Depending on the type of
application request, the field-specific data and environmental data
for one or more fields may be located in the content management
servers, account servers, environmental data servers, the
corresponding databases, and, in some instances, archived in the
modeling servers and/or application servers. Based on the
simulations generated by the modeling servers, field condition data
and/or agricultural intelligence services for one or more fields is
provided to the application servers for transmission to the
requesting user device via the network. More specifically, the user
may use the user device to access a plurality of windows or
displays showing field condition data and/or agricultural
intelligence services, as described below.
[0045] Although the aforementioned application platform has been
configured with various example embodiments above, one skilled in
the art will appreciate that any configuration of servers may be
possible and that example embodiments of the present disclosure
need not be limited to the configurations disclosed herein.
[0046] Field Condition Data
[0047] Field Weather and Temperature Conditions
[0048] As part of the field condition data provided, the
agricultural intelligence computer system tracks field weather
conditions for each field identified by the user. The agricultural
intelligence computer system determines current weather conditions
including field temperature, wind, humidity, and dew point. The
agricultural intelligence computer system also determines
forecasted weather conditions including field temperature, wind,
humidity, and dew point for hourly projected intervals, daily
projected intervals, or any interval specified by the user. The
forecasted weather conditions are also used to forecast field
precipitation, field workability, and field growth stage. Near-term
forecasts are determined using a meteorological model (e.g., the
Microcast model) while long-term projections are determined using
historical analog simulations.
[0049] The agricultural intelligence computer system uses grid
temperatures to determine temperature values. Known research shows
that using grid techniques provides more accurate temperature
measurements than point-based temperature reporting. Temperature
grids are typically square physical regions, typically 2.5 miles by
2.5 miles. The agricultural intelligence computer system associates
the field with a temperature grid that contains the field. The
agricultural intelligence computer system identifies a plurality of
weather stations that are proximate to the temperature grid. The
agricultural intelligence computer system receives temperature data
from the plurality of weather stations. The temperatures reported
by the plurality of weather stations are weighted based on their
relative proximity to the grid such that more proximate weather
stations have higher weights than less proximate weather stations.
Further, the relative elevation of the temperature grid is compared
to the elevation of the plurality of weather stations. Temperature
values reported by the plurality of weather stations are adjusted
in response to the relative difference in elevation. In some
examples, the temperature grid includes or is adjacent to a body of
water. Bodies of water are known to cause a reduction in the
temperature of an area. Accordingly, when a particular field is
proximate to a body of water as compared to the weather station
providing the temperature reading, the reported temperature for the
field is adjusted downwards to account for the closer proximity to
the body of water.
[0050] Precipitation values are similarly determined using
precipitation grids that utilize meteorological radar data.
Precipitation grids have similar purposes and characteristics as
temperature grids. Specifically, the agricultural intelligence
computer system uses available data sources such as the National
Weather Service's NEXRAD Doppler radar data, rain gauge networks,
and weather stations across the U.S. The agricultural intelligence
computer system further validates and calibrates reported data with
ground station and satellite data. In the example embodiment, the
Doppler radar data is obtained for the precipitation grid. The
Doppler radar data is used to determine an estimate of
precipitation for the precipitation grid. The estimated
precipitation is adjusted based on other data sources such as other
weather radar sources, ground weather stations (e.g., rain gauges),
satellite precipitation sources (e.g., the National Oceanic and
Atmospheric Administration's Satellite Applications and Research),
and meteorological sources. By utilizing multiple distinct data
sources, more accurate precipitation tracking may be
accomplished.
[0051] Current weather conditions and forecasted weather conditions
(hourly, daily, or as specified by the user) are displayed on the
user device graphically along with applicable information regarding
the specific field, such as field name, crop, acreage, field
precipitation, field workability, field growth stage, soil
moisture, and any other field definition data or field-specific
data that the user may specify. Such information may be displayed
on the user device in one or more combinations and level of detail
as specified by the user.
[0052] In an example embodiment, temperature can be displayed as
high temperatures, average temperatures and low temperatures over
time. Temperature can be shown during a specific time and/or date
range and/or harvest year and compared against prior times, years,
including a 5 year average, a 15 year average, a 30 year average or
as specified by the user.
[0053] In an example embodiment, precipitation can be displayed as
the amount of precipitation and/or accumulated precipitation over
time. Precipitation can be shown during a specific time period
and/or date range and/or harvest year and compared against prior
times, years, including a 5 year average, a 15 year average, a 30
year average or as specified by the user. Precipitation can also be
displayed as past and future radar data. In an example embodiment,
past radar may be displayed over the last 1.5 hours or as specified
by the user. Future radar may be displayed over the next 6 hours or
as specified by the user. Radar may be displayed as an overlay of
an aerial image map showing the user's one or more fields where the
user has the ability to zoom in and out of the map. Radar can be
displayed as static at intervals selected by the user or
continuously over intervals selected by the user. The underlying
radar data received and/or processed by the agricultural
intelligence computer system may be in the form of Gridded Binary
(GRIB) files that includes forecast reflectivity files,
precipitation type, and precipitation-typed reflectivity
values.
[0054] Field Workability Conditions Data
[0055] As part of the field condition data, the agricultural
intelligence computer system provides field workability conditions,
which indicate the degree to which a field or section of a field
(associated with the field definition data) may be worked for a
given time of year using machinery or other implements. In an
example embodiment, the agricultural intelligence computer system
retrieves field historical precipitation data over a predetermined
period of time, field predicted precipitation over a predetermined
period of time, and field temperatures over a predetermined period
of time. The retrieved data is used to determine one or more
workability index.
[0056] In an example embodiment, the workability index may be used
to derive three values of workability for particular farm
activities. The value of "Good" workability indicates high
likelihood that field conditions are acceptable for use of
machinery or a specified activity during an upcoming time interval.
The value of "Check" workability indicates that field conditions
may not be ideal for the use of machinery or a specified activity
during an upcoming time interval. The value of "Stop" workability
indicates that field conditions are not suitable for work or a
specified activity during an upcoming time interval.
[0057] Determined values of workability may vary depending upon the
farm activity. For example, planting and tilling typically require
a low level of muddiness and may require a higher workability index
to achieve a value of "Good" than activities that allow for a
higher level of muddiness. In some embodiments, workability indices
are distinctly calculated for each activity based on a distinct set
of factors. For example, a workability index for planting may
correlate to predicted temperature over the next 60 hours while a
workability index for harvesting may be correlated to precipitation
alone. In some examples, user may be prompted at the user device to
answer questions regarding field activities if such information has
not already been provided to the agricultural intelligence computer
system. For example, a user may be asked what field activities are
currently in use. Depending upon the response, the agricultural
intelligence computer system may adjust its calculations of the
workability index because of the user's activities, thereby
incorporating the feedback of the user into the calculation of the
workability index. Alternately, the agricultural intelligence
computer system may adjust the recommendations made to the user for
activities. In a further example, the agricultural intelligence
computer system may recommend that the user stop such activities
based on the responses.
[0058] Field Growth Stage Conditions
[0059] As part of the field condition data provided, the
agricultural intelligence computer system provides field growth
stage conditions (e.g., for corn, vegetative (VE-VT) and
reproductive (R1-R6) growth stages) for the crops being grown in
each listed field. Vegetative growth stages for corn typically are
described as follows. The "VE" stage indicates emergence, the "V1"
stage indicates a first fully expanded leaf with a leaf collar; the
"V2" stage indicates a second fully expanded leaf with the leaf
collar; the "V3" stage indicates a third fully expanded leaf with
the leaf collar; any "V(n)" stage indicates an nth fully expanded
leaf with the leaf collar; and the "VT" stage indicates that the
tassel of the corn is fully emerged. In the reproductive growth
stage model described, "R1" indicates a silking period in which
pollination and fertilization processes take place; the "R2" or
blister stage (occurring 10-14 days after R1) indicates that the
kernel of corn is visible and resembles a blister; the "R3" or milk
stage (occurring 18-22 days after R1) indicates that the kernel is
yellow outside and contains milky white fluid; the "R4" or dough
stage (occurring 24-28 days after R1) indicates that the interior
of the kernel has thickened to a dough-like consistency; the "R5"
or dent stage (occurring 35-42 days after R1) indicates that the
kernels are indented at the top and beginning drydown; and the "R6"
or physiological maturity stage (occurring 55-65 days after R1)
indicates that kernels have reached maximum dry matter
accumulation. Field growth stage conditions may be used to
determine timing of key farming decisions. The agricultural
intelligence computer system computes crop progression for each
crop through stages of growth (agronomic stages) by tracking the
impact of weather (both historic and forecasted) on the
phenomenological development of the crop from planting through
harvest.
[0060] In the example embodiment, the agricultural intelligence
computer system uses the planting date entered by the user device
to determine field growth stage conditions. In other words, the
user may enter the planting date into the user device, which
communicates the planting date to the agricultural intelligence
computer system. Alternately, the agricultural intelligence
computer system may estimate the planting date using a system
algorithm. Specifically, the planting date may be estimated based
on agronomic stage data and planting practices in the region
associated with the field definition data. The planting practices
may be received from a data service such as a university data
network that monitors typical planting techniques for a region. The
agricultural intelligence computer system further uses data
regarding the user's farming practices within the current season
and for historical seasons, thereby facilitating historical
analysis. In other words, the agricultural intelligence computer
system is configured to use historical practices of each particular
grower on a subject field or to alternately use historical
practices for the corresponding region to predict the planting date
of a crop when the actual planting date is not provided by the
grower. The agricultural intelligence computer system determines a
relative maturity value of the crops based on expected heat units
over the growing season in light of the planting date, the user's
farming practices, and field-specific data. As heat is a proxy for
energy received by crops, the agricultural intelligence computer
system calculates expected heat units for crops and determines a
development of maturity of the crops. In the example embodiment,
maximum temperatures and low temperatures are used to estimate heat
units.
[0061] Soil Moisture
[0062] As part of the field condition data, the agricultural
intelligence computer system determines and provides soil moisture
data via a display showing a client application on the user device.
Soil moisture indicates the percent of total water capacity
available to the crop that is present in the soil of the field.
Soil moisture values are initialized at the beginning of the
growing season based on environmental data in the agricultural
intelligence computer system at that time, such as data from the
North American Land Data Assimilation System, and field-specific
data. In another embodiment, a soil analysis computing device may
analyze soil samples from a plurality of fields for a grower
wherein the plurality of fields includes a selected field. Once
analyzed, the results may be directly provided from the soil
analysis computing device to the agricultural intelligence computer
system so that the soil analysis results may be provided to the
grower. Further, data from the soil analysis may be inputted into
the agricultural intelligence computer system for use in
determining field condition data and agricultural intelligence
services.
[0063] Soil moisture values are then adjusted, at least daily,
during the growing season by tracking moisture entering the soil
via precipitation and moisture leaving the soil via
evapotranspiration (ET).
[0064] In some examples, water that is received in an area as
precipitation does not enter the soil because it is lost as run
off. Accordingly, in one example, a gross and net precipitation
value is calculated. Gross precipitation indicates a total
precipitation value. Net precipitation excludes a calculated amount
of water that never enters the soil because it is lost as runoff. A
runoff value is determined based on the precipitation amount over
time and a curve determined by the USDA classification of soil
type. The systems account for a user's specific field-specific data
related to soil to determine runoff and the runoff curve for the
specific field. Soil input data, described above, may alternately
be provided via the soil analysis computing device. Lighter,
sandier soils allow greater precipitation water infiltration and
experience less runoff during heavy precipitation events than
heavier, more compact soils. Heavier or denser soil types have
lower precipitation infiltration rates and lose more precipitation
to runoff on days with large precipitation events.
[0065] Daily evapotranspiration associated with a user's specific
field is calculated based on a version of the standard
Penman-Monteith ET model. The total amount of water that is
calculated as leaving the soil through evapotranspiration on a
given day is based on the following: [0066] 1. Maximum and minimum
temperatures for the day: Warmer temperatures result in greater
evapotranspiration values than cooler temperatures. [0067] 2.
Latitude: During much of the corn growing season, fields at more
northern latitudes experience greater solar radiation than fields
at more southern latitudes due to longer days. But fields at more
northern latitudes also get reduced radiation due to earth tilting.
Areas with greater net solar radiation values will have relatively
higher evapotranspiration values than areas with lower net solar
radiation values. [0068] 3. Estimated crop growth stage: Growth
stages around pollination provide the highest potential daily
evapotranspiration values while growth stages around planting and
late in grain fill result in relatively lower daily
evapotranspiration values, because the crop uses less water in
these stages of growth. [0069] 4. Current soil moisture: The
agricultural intelligence computer system's model accounts for the
fact that crops conserve and use less water when less water is
available in the soil. The reported soil moisture values reported
that are above a certain percentage, determined by crop type,
provide the highest potential evapotranspiration values and
potential evapotranspiration values decrease as soil moisture
values approach 0%. As soil moisture values fall below this
percentage, corn will start conserving water and using soil
moisture at less than optimal rates. This water conservation by the
plant increases as soil moisture values decrease, leading to lower
and lower daily evapotranspiration values. [0070] 5. Wind:
Evapotranspiration takes into account wind; however,
evapotranspiration is not as sensitive to wind as to the other
conditions. In an example embodiment, a set wind speed of 2 meters
per second is used for all evapotranspiration calculations.
[0071] Alerts and Reporting
[0072] The agricultural intelligence computer system is
additionally configured to provide alerts based on weather and
field-related information. Specifically, the user may define a
plurality of thresholds for each of a plurality of alert
categories. When field condition data indicates that the thresholds
have been exceeded, the user device will receive alerts. Alerts may
be provided via the application (e.g., notification upon login,
push notification), email, text messages, or any other suitable
method. Alerts may be defined for crop cultivation monitoring, for
example, hail size, rainfall, overall precipitation, soil moisture,
crop scouting, wind conditions, field image, pest reports or
disease reports. Alternately, alerts may be provided for crop
growth strategy. For example, alerts may be provided based on
commodity prices, grain prices, workability indexes, growth stages,
and crop moisture content. In some examples, an alert may indicate
a recommended course of action. For example, the alert may
recommend that field activities (e.g., planting, nitrogen
application, pest and disease treatment, irrigation application,
scouting, or harvesting) occur within a particular period of time.
The agricultural intelligence computer system is also configured to
receive information on farming activities from, for example, the
user device, an agricultural machine and/or agricultural machine
computing device, or any other source. Accordingly, alerts may also
be provided based on logged farm activity such as planting,
nitrogen application, spraying, irrigation, scouting, or
harvesting. In some examples, alerts may be provided regardless of
thresholds to indicate certain field conditions. In one example, a
daily precipitation, growth stage, field image or temperature alert
may be provided to the user device.
[0073] The agricultural intelligence computer system is further
configured to generate a plurality of reports based on field
condition data. Such reports may be used by the user to improve
strategy and decision-making in farming. The reports may include
reports on crop growth stage, temperature, humidity, soil moisture,
precipitation, workability, pest risk, and disease risk. The
reports may also include one or more field definition data,
environmental data, field-specific data, scouting and logging
events, field condition data, summary of agricultural intelligence
services or FSA Form 578.
[0074] Scouting and Notes
[0075] The agricultural intelligence computer system is also
configured to receive supplemental information from the user
device. For example, a user may provide logging or scouting events
regarding the fields associated with the field definition data. The
user may access a logging application at the user device and update
the agricultural intelligence computer system. In one embodiment,
the user accesses the agricultural intelligence computer system via
a user device while being physically located in a field to enter
field-specific data. The agricultural intelligence computer system
might automatically display and transmit the date and time and
field definition data associated with the field-specific data, such
as geographic coordinates and boundaries. The user may provide
general data for activities including field, location, date, time,
crop, images, and notes. The user may also provide data specific to
particular activities such as planting, nitrogen application,
pesticide application, harvesting, scouting, and current weather
observations. Such supplemental information may be associated with
the other data networks and used by the user for analysis.
[0076] The agricultural intelligence computer system is
additionally configured to display scouting and logging events
related to the receipt of field-specific data from the user via one
or more agricultural machines or agricultural machine devices that
interacts with the agricultural intelligence computer system or via
the user device. Such information can be displayed as specified by
the user. In one example, the information is displayed on a
calendar on the user device, wherein the user can obtain further
details regarding the information as necessary. In another example,
the information is displayed in a table on the user device, wherein
the user can select the specific categories of information that the
user would like displayed.
[0077] The agricultural intelligence computer system also includes
(or is in data communication with) a plurality of modules
configured to analyze field condition data and other data available
to the agricultural intelligence computer system and to recommend
certain agricultural actions (or activities) to be performed
relative to the fields being analyzed in order to maximize yield
and/or revenue for the particular fields. In other words, such
modules review field condition data and other data to recommend how
to effectively enhance output and performance of the particular
fields. The modules may be variously referred to as agricultural
intelligence modules or, alternately as recommendation advisor
components or agricultural intelligence services. As used herein,
such agricultural intelligence modules may include, but are not
limited to a) planting advisor module, b) nitrogen application
advisor module, c) pest advisor module, d) field health advisor
module, e) harvest advisor module, and f) revenue advisor
module.
[0078] Agricultural Intelligence Services
[0079] Planting Advisor Module
[0080] The agricultural intelligence computer system is
additionally configured to provide agricultural intelligence
services related to planting. In one example embodiment, a planting
advisor module provides planting date recommendations. The
recommendations are specific to the location of the field and adapt
to the current field condition data, along with weather predicted
to be experienced by the specific fields.
[0081] In one embodiment, the planting advisor module receives one
or more of the following data points for each field identified by
the user (as determined from field definition data) in order to
determine and provide such planting date recommendations: [0082] 1.
A first set of data points is seed characteristic data. Seed
characteristic data may include any relevant information related to
seeds that are planted or will be planted. Seed characteristic data
may include, for example, seed company data, seed cost data, seed
population data, seed hybrid data, seed maturity level data, seed
disease resistance data, and any other suitable seed data. Seed
company data may refer to the manufacturer or provider of seeds.
Seed cost data may refer to the price of seeds for a given
quantity, weight, or volume of seeds. Seed population data may
include the amount of seeds planted (or intended to be planted) or
the density of seeds planted (or intended to be planted). Seed
hybrid data may include any information related to the biological
makeup of the seeds (i.e., which plants have been hybridized to
form a given seed.) Seed maturity level data may include, for
example, a relative maturity level of a given seed (e.g., a
comparative relative maturity ("CRM") value or a silk comparative
relative maturity ("silk CRM")), growing degree units ("GDUs")
until a given stage such as silking, mid-pollination, black layer,
or flowering, and a relative maturity level of a given seed at
physiological maturity ("Phy. CRM"). Disease resistance data may
include any information related to the resistance of seeds to
particular diseases. In the example embodiment, disease resistance
data includes data related to the resistance to Gray Leaf Spot,
Northern Leaf Blight, Anthracnose Stalk Rot, Goss's Wilt, Southern
Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight,
Southern Rust, Southern Virus Complex, Stewart's Leaf Blight, Corn
Lethal Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown
Rot. Other suitable seed data may include, for example, data
related to, grain drydown, stalk strength, root strength, stress
emergence, staygreen, drought tolerance, ear flex, test eight,
plant height, ear height, mid-season brittle stalk, plant vigor,
fungicide response, growth regulators sensitivity, pigment
inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing,
kernel texture, emergence, harvest appearance, harvest population,
seedling growth, cob color, and husk cover. [0083] 2. A second set
of data points is field-specific data related to soil composition.
Such field-specific data may include measurements of the acidity or
basicity of soil (e.g., pH levels), soil organic matter levels
("OM" levels), and cation exchange capacity levels ("CEC" levels).
[0084] 3. A third set of data points is field-specific data related
to field data. Such field-specific data may include field names and
identifiers, soil types or classifications, tilling status,
irrigation status. [0085] 4. A fourth set of data points is
field-specific data related to historical harvest data. Such
field-specific data may include crop type or classification,
harvest date, actual production history ("APH"), yield, grain
moisture, and tillage practice. [0086] 5. In some examples, users
may be prompted at the user device to provide a fifth set of data
points by answering questions regarding desired planting population
(e.g., total crop volume and total crop density for a particular
field) and/or seed cost, expected yield, and indication of risk
preference (e.g., general or specific: user is willing to risk a
specific number of bushels per acre to increase the chance of
producing a specific larger number of bushels per acre) if such
information has not already been provided to the agricultural
intelligence computer system.
[0087] The planting advisor module receives and processes the sets
of data points to simulate possible yield potentials. Possible
yield potentials are calculated for various planting dates. The
planting advisor module additionally utilizes additional data to
generate such simulations. The additional data may include
simulated weather between the planting data and harvesting date,
field workability, seasonal freeze risk, drought risk, heat risk,
excess moisture risk, estimated soil temperature, and/or risk
tolerance. The likely harvesting date may be estimated based upon
the provided relative maturity (e.g., to generate an earliest
recommended harvesting date) and may further be adjusted based upon
predicted weather and workability. Risk tolerance may be calculated
based for a high profit/high risk scenario, a low risk scenario, a
balanced risk/profit scenario, and a user defined scenario. The
planting advisor module generates such simulations for each
planting date and displays a planting date recommendation for the
user on the user device. The recommendation includes the
recommended planting date, projected yield, relative maturity, and
graphs the projected yield against planting date. In some examples,
the planting advisor module also graphs planting dates against the
projected yield loss resulting from spring freeze risk, fall freeze
risk, drought risk, heat risk, excess moisture risk, and estimated
soil temperature. In some examples, such graphs are generated based
on the predicted temperatures and/or precipitation between each
planting date and a likely or earliest recommended harvest date for
the selected relative maturity. The planting advisor module
provides the option of modeling and displaying alternative yield
scenarios for planting data and projected yield by modifying one or
more data points associated with seed characteristic data,
field-specific data, desired planting population and/or seed cost,
expected yield, and/or indication of risk preference. The
alternative yield scenarios may be displayed and graphed on the
user device along with the original recommendation.
[0088] In some examples, the planting advisor module recommends or
excludes planting dates based on predicted workability. For
example, dates at which a predicted planting-specific workability
value is "Stop" may either be excluded or not recommended. In some
examples, the planting advisor recommends or excludes planting
dates based upon predicted weather events (e.g., temperature or
precipitation). For examples, planting dates may be recommended
after which likelihood of freezing is lower than associated
threshold values.
[0089] In some examples, the planting advisor recommends seed
characteristics or graphs estimated yield against planting date for
various seed characteristics. For example, a graph of estimated
yield against planting date may be generated for both the seed
characteristic and a recommended seed characteristic. The
recommended seed characteristic may be recommended based on any of
the maximum yield at any planting date, the maximum average yield
across a set of planting dates, or the earliest possible harvesting
date (e.g., where a later harvesting date is not desired due to
predicted weather, a relative maturity may be selected in order to
enable a desired harvesting date).
[0090] Nitrogen Application Advisor Module
[0091] The agricultural intelligence computer system is
additionally configured to provide agricultural intelligence
services related to soil. The nitrogen application advisor module
determines potential needs for nitrogen in the soil and recommends
nitrogen application practices to a user. More specifically, the
nitrogen application advisor module is configured to identify
conditions when crop needs cannot be met by nitrogen present in the
soil. In one example embodiment, a nitrogen application advisor
module provides recommendations for sidedressing or spraying, such
as date and rate, specific to the location of the field and adapted
to the current field condition data. In one embodiment, the
nitrogen application advisor module is configured to receive one or
more of the following data points for each field identified by the
user (as determined from field definition data): [0092] 1. A first
set of data points includes environmental information.
Environmental information may include information related to
weather, precipitation, meteorology, soil and crop phenology.
[0093] 2. A second set of data points includes field-specific data
related to field data. Such field-specific data may include field
names and identifiers, soil types or classifications, tilling
status, irrigation status. [0094] 3. A third set of data points
includes field-specific data related to historical harvest data.
Such field-specific data may include crop type or classification,
harvest date, actual production history ("APH"), yield, grain
moisture, and tillage practice. [0095] 4. A fourth set of data
points is field-specific data related to soil composition. Such
field-specific data may include measurements of the acidity or
basicity of soil (e.g., pH levels), soil organic matter levels
("OM" levels), and cation exchange capacity levels ("CEC" levels).
[0096] 5. A fifth set of data points is field-specific data related
to planting data. Such field-specific data may include planting
date, seed type or types, relative maturity (RM) levels of planted
seed(s), and seed population. In some examples, the planting data
is transmitted from a planter monitor to the agricultural
intelligence computer system 150, e.g., via a cellular modem or
other data communication device of the planter monitor. [0097] 6. A
sixth set of data points is field-specific data related to nitrogen
data. Such field-specific data may include nitrogen application
dates, nitrogen application amounts, and nitrogen application
sources. [0098] 7. A seventh set of data points is field-specific
data related to irrigation data. Such field-specific data may
include irrigation application dates, irrigation amounts, and
irrigation sources.
[0099] Based on the sets of data points, the nitrogen application
advisor module determines a nitrogen application recommendation. As
described below, the recommendation includes a list of fields with
adequate nitrogen, a list of fields with inadequate nitrogen, and a
recommended nitrogen application for the fields with inadequate
nitrogen.
[0100] In some examples, users may be prompted at the user device
to answer questions regarding nitrogen application (e.g.,
side-dressing, spraying) practices and costs, such as type of
nitrogen (e.g., Anhydrous Ammonia, Urea, UAN (Urea Ammonium
Nitrate) 28%, 30% or 32%, Ammonium Nitrate, Ammonium Sulphate,
Calcium Ammonium Sulphate), nitrogen costs, latest growth stage of
crop at which nitrogen can be applied, application equipment, labor
costs, expected crop price, tillage practice (e.g., type
(conventional, no till, reduced, strip) and amount of surface of
the field that has been tilled), residue (the amount of surface of
the field covered by residue), related farming practices (e.g.,
manure application, nitrogen stabilizers, cover crops) as well as
prior crop data (e.g., crop type, harvest date, Actual Production
History (APH), yield, tillage practice), current crop data (e.g.,
planting date, seed(s) type, relative maturity (RM) of planted
seed(s), seed population), soil characteristics (pH, OM, CEC) if
such information has not already been provided to the agricultural
intelligence computer system. For certain questions, such as latest
growth stage of crop at which nitrogen can be applied, application
equipment, labor costs, the user has the option to provide a
plurality of alternative responses to that the agricultural
intelligence computer system can optimize the nitrogen application
advisor recommendation.
[0101] Using the environmental information, field-specific data,
nitrogen application practices and costs, prior crop data, current
crop data, and/or soil characteristics, the agricultural
intelligence computer system identifies the available nitrogen in
each field and simulates possible nitrogen application practices,
dates, rates, and next date on which workability for a nitrogen
application is "Green" taking into account predicted workability
and nitrogen loss through leaching, denitrification and
volatilization. The nitrogen application advisor module generates
and displays on the user device a nitrogen application
recommendation for the user. The recommendation includes: [0102] 1.
The list of fields having enough nitrogen, including for each field
the available nitrogen, last application data, and the last
nitrogen rate applied. [0103] 2. The list of fields where nitrogen
application is recommended, including for each field the available
nitrogen, recommended application practice, recommended application
dates, recommended application rate, and next data on which
workability for the nitrogen application is "Green."
[0104] The user has the option of modeling (i.e., running a model)
and displaying nitrogen lost (total and divided into losses
resulting from volatilization, denitrification, and leaching) and
crop use ("uptake") of nitrogen over a specified time period
(predefined or as defined by the user) for the recommended nitrogen
application versus one or more alternative scenarios based on a
custom application practice, date and rate entered by the user. The
user has the option of modeling and displaying estimated return on
investment for the recommended nitrogen application versus one or
more alternative scenarios based on a custom application practice,
date and rate entered by the user. The alternative nitrogen
application scenarios may be displayed and graphed on the user
device along with the original recommendation. The user has the
further option of modeling and displaying estimated yield benefit
(minimum, average, and maximum) for the recommended nitrogen
application versus one or more alternative scenarios based on a
custom application practice, date and rate entered by the user. The
user has the further option of modeling and displaying estimated
available nitrogen over any time period specified by the user for
the recommended nitrogen application versus one or more alternative
scenarios based on a custom application practice, date and rate
entered by the user. The user has the further option of running the
nitrogen application advisor (using the nitrogen application
advisor) for one or more sub-fields or management zones within a
field.
[0105] Pest Advisor Module (or Pest and Disease Advisor Module)
[0106] The agricultural intelligence computer system is
additionally configured to provide agricultural intelligence
services related to pest and disease. The pest and disease advisor
module is configured to identify risks posed to crops by pest
damage and/or disease damage. In an example embodiment, the pest
and disease advisor module identifies risks caused by the pests
that cause that the most economic damage to crops in the U.S. Such
pests include, for example, corn rootworm, corn earworm, soybean
aphid, western bean cutworm, European corn borer, armyworm, bean
leaf beetle, Japanese beetle, and twospotted spider mite. In some
examples, the pest and disease advisor provides supplemental
analysis for each pest segmented by growth stages (e.g., larval and
adult stages). The pest and disease advisor module also identifies
disease risks caused by the diseases that cause that the most
economic damage to crops in the U.S. Such diseases include, for
example, Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk
Rot, Goss's Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust,
Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex,
Stewart's Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear
Rot, Fusarium Crown Rot. The pest advisor is also configured to
recommend scouting practices and treatment methods to respond to
such pest and disease risks. The pest advisor is also configured to
provide alerts based on observations of pests in regions proximate
to the user's fields.
[0107] In one embodiment, the pest and disease advisor may receive
one or more of the following sets of data for each field identified
by the user (as determined from field definition data): [0108] 1. A
first set of data points is environmental information.
Environmental information includes information related to weather,
precipitation, meteorology, crop phenology and pest and disease
reporting. [0109] 2. A second set of data points is seed
characteristic data. Seed characteristic data may include any
relevant information related to seeds that are planted or will be
planted. Seed characteristic data may include, for example, seed
company data, seed cost data, seed population data, seed hybrid
data, seed maturity level data, seed disease resistance data, and
any other suitable seed data. Seed company data may refer to the
manufacturer or provider of seeds. Seed cost data may refer to the
price of seeds for a given quantity, weight, or volume of seeds.
Seed population data may include the amount of seeds planted (or
intended to be planted) or the density of seeds planted (or
intended to be planted). Seed hybrid data may include any
information related to the biological makeup of the seeds (i.e.,
which plants have been hybridized to form a given seed.) Seed
maturity level data may include, for example, a relative maturity
level of a given seed (e.g., a comparative relative maturity
("CRM") value or a silk comparative relative maturity ("silk
CRM")), growing degree units ("GDUs") until a given stage such as
silking, mid-pollination, black layer, or flowering, and a relative
maturity level of a given seed at physiological maturity ("Phy.
CRM"). Disease resistance data may include any information related
to the resistance of seeds to particular diseases. In the example
embodiment, disease resistance data includes data related to the
resistance to Gray Leaf Spot, Northern Leaf Blight, Anthracnose
Stalk Rot, Goss's Wilt, Southern Corn Leaf Blight, Eyespot, Common
Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus
Complex, Stewart's Leaf Blight, Corn Lethal Necrosis, Headsmut,
Diplodia Ear Rot, and Fusarium Crown Rot. Other suitable seed data
may include, for example, data related to, grain drydown, stalk
strength, root strength, stress emergence, staygreen, drought
tolerance, ear flex, test eight, plant height, ear height,
mid-season brittle stalk, plant vigor, fungicide response, growth
regulators sensitivity, pigment inhibitors, sensitivity,
sulfonylureas sensitivity, harvest timing, kernel texture,
emergence, harvest appearance, harvest population, seedling growth,
cob color, and husk cover. [0110] 3. A third set of data points is
field-specific data related to planting data. Such field-specific
data may include, for example, planting dates, seed type, relative
maturity (RM) of planted seed, and seed population. [0111] 4. A
fourth set of data points is field-specific data related to
pesticide data. Such field-specific data may include, for example,
pesticide application date, pesticide product type (specified by,
e.g., EPA registration number), pesticide formulation, pesticide
usage rate, pesticide acres tested, pesticide amount sprayed, and
pesticide source.
[0112] In some examples, users may be prompted at the user device
to answer questions regarding pesticide application practices and
costs, such as type of product type, application date, formulation,
rate, acres tested, amount, source, costs, latest growth stage of
crop at which pesticide can be applied, application equipment,
labor costs, expected crop price as well as current crop data
(e.g., planting date, seed(s) type, relative maturity (RM) of
planted seed(s), seed population) if such information has not
already been provided to the agricultural intelligence computer
system. Accordingly, the pest and disease advisor module receives
such data from user devices. For certain questions, such as latest
growth stage of crop at which pesticide can be applied, application
equipment, labor costs, the user has the option to provide a
plurality of alternative responses to that the agricultural
intelligence computer system can optimize the pest and disease
advisor recommendation.
[0113] The pest and disease advisor module is configured to receive
and process all such sets of data points and received user data and
simulate possible pesticide application practices. The simulation
of possible pesticide practices includes, dates, rates, and next
date on which workability for a pesticide application is "Green"
taking into account predicted workability. The pest and disease
advisor module generates and displays on the user device a scouting
and treatment recommendation for the user. The scouting
recommendation includes daily (or as specified by the user) times
to scout for specific pests and diseases. The user has the option
of displaying a specific subset of pests and diseases as well as
additional information regarding a specific pest or disease. The
treatment recommendation includes the list of fields where a
pesticide application is recommended, including for each field the
recommended application practice, recommended application dates,
recommended application rate, and next data on which workability
for the pesticide application is "Green." The user has the option
of modeling and displaying estimated return on investment for the
recommended pesticide application versus one or more alternative
scenarios based on a custom application practice, date and rate
entered by the user. The alternative pesticide application
scenarios may be displayed and graphed on the user device along
with the original recommendation. The user has the further option
of modeling and displaying estimated yield benefit (minimum,
average, and maximum) for the recommended pesticide application
versus one or more alternative scenarios based on a custom
application practice, date and rate entered by the user.
[0114] Field Health Advisor Module
[0115] The field health advisor module identifies crop health
quality over the course of the season and uses such crop health
determinations to recommend scouting or investigation in areas of
poor field health. More specifically, the field health advisor
module receives and processes field image data to determine,
identify, and provide index values of biomass health. The index
values of biomass health may range from zero (indicating no
biomass) to 1 (indicating the maximum amount of biomass). In an
example embodiment, the index value has a specific color scheme, so
that every image has a color-coded biomass health scheme (e.g.,
brown areas show the areas in the field with the lowest relative
biomass health). In one embodiment, the field health advisor module
may receive one or more of the following data points for each field
identified by the user (as determined from field definition data):
[0116] 1. A first set of data points includes environmental
information. Such environmental information includes information
related to satellite imagery, aerial imagery, terrestrial imagery
and crop phenology. [0117] 2. A second set of data points includes
field-specific data related to field data. Such field-specific data
may include field and soil identifiers such as field names, and
soil types. [0118] 3. A third set of data points includes
field-specific data related to soil composition data. Such
field-specific data may include measurements of the acidity or
basicity of soil (e.g., pH levels), soil organic matter levels
("OM" levels), and cation exchange capacity levels ("CEC" levels).
[0119] 4. A fourth set of data points includes field-specific data
related to planting data. Such field-specific data may include, for
example, planting dates, seed type, relative maturity (RM) of
planted seed, and seed population.
[0120] The field health advisor module receives and processes all
such data points (along with field image data) to determine and
identify a crop health index for each location in each field
identified by the user each time a new field image is available. In
an example embodiment, the field health advisor module determines a
crop health index as a normalized difference vegetation index
("NDVI") based on at least one near-infrared ("NIR") reflectance
value and at least one visible spectrum reflectance value at each
raster location in the field. In another example embodiment, the
crop health index is a NDVI based on multispectral reflectance.
[0121] The field health advisor module generates and displays on
the user device the health index map as an overlay on an aerial map
for each field identified by the user. In an example embodiment,
for each field, the field health advisor module will display field
image date, growth stage of crop at that time, soil moisture at
that time, and health index map as an overlay on an aerial map for
the field. In an example embodiment, the field image resolution is
between 5 m and 0.25 cm. The user has the option of modeling and
displaying a list of fields based on field image date and/or crop
health index (e.g., field with lowest overall health index values
to field with highest overall health index values, field with
highest overall health index values to field with lowest overall
health index values, lowest health index value variability within
field, highest health index value variability within field, or as
specified by the user). The user also has the option of modeling
and displaying a comparison of crop health index for a field over
time (e.g., side-by-side comparison, overlay comparison). In an
example embodiment, the field health advisor module provides the
user with the ability to select a location on a field to get more
information about the health index, soil type or elevation at a
particular location. In an example embodiment, the field health
advisor module provides the user with the ability to save a
selected location, the related information, and a short note so
that the user can retrieve the same information on the user device
while in the field.
[0122] A technical effect of the systems and methods described
herein include at least one of (a) improved utilization of
agricultural fields through improved field condition monitoring;
(b) improved selection of time and method of fertilization; (c)
improved selection of time and method of pest control; (d) improved
selection of seeds planted for the given location of soil; (e)
improved field condition data for at a micro-local level; and (f)
improved selection of time of harvest.
[0123] More specifically, the technical effects can be achieved by
performing at least one of the following steps: (a) receiving a
plurality of field definition data, retrieving a plurality of input
data from a plurality of data networks, determining a field region
based on the field definition data, identifying a subset of the
plurality of input data associated with the field region,
determining a plurality of field condition data based on the subset
of the plurality of input data, and providing the plurality of
field condition data to the user device; (b) defining a
precipitation analysis period, retrieving a set of recent
precipitation data, a set of predicted precipitation data, and a
set of temperature data associated with the precipitation analysis
period from the subset of the plurality of input data, determining
a workability index based on the set of recent precipitation data,
the set of predicted precipitation data, and the set of temperature
data, and providing a workability value to the user device based on
the workability index; (c) receiving a prospective field activity,
and determining the workability index based partially on the
prospective field activity; (d) determining an initial crop
moisture level, receiving a plurality of daily high and low
temperatures, receiving a plurality of crop water usage, and
determining a soil moisture level; (e) receiving a plurality of
alert preferences from the user device, identifying a plurality of
alert thresholds associated with the plurality of alert
preferences, monitoring the subset of the plurality of input data,
and alerting the user device when at least one of the alert
thresholds is exceeded; (f) receiving a plurality of field
definition data from at least one of a user device and an
agricultural machine device; (g) identifying a grid associated with
the field region, identifying, from a plurality of weather stations
associated with the grid, wherein each of the plurality of weather
stations is associated with a weather station location, identifying
an associated weight for each of the plurality of weather stations
based on each associated weather station location, receiving a
temperature reading from each of the plurality of weather stations,
and identifying a temperature value for the field region based on
the plurality of temperature readings and each associated weight;
(h) receiving a plurality of field definition data, retrieving a
plurality of input data from a plurality of data networks,
determining a field region based on the field definition data,
identifying a subset of the plurality of input data associated with
the field region, determining a plurality of field condition data
based on the subset of the plurality of input data, identifying a
plurality of field activity options, determining a recommendation
score for each of the plurality of field activity options based at
least in part on the plurality of field condition data, and
providing a recommended field activity option from the plurality of
field activity options based on the plurality of recommendation
scores; (i) defining a precipitation analysis period, retrieving a
set of recent precipitation data, a set of predicted precipitation
data, and a set of temperature data associated with the
precipitation analysis period from the subset of the plurality of
input data, determining a workability index based on the set of
recent precipitation data, the set of predicted precipitation data,
and the set of temperature data, and identifying a recommended
agricultural activity based, at least in part, on the workability
index; (j) determining an initial crop moisture level, receiving a
plurality of daily high and low temperatures, receiving a plurality
of crop water usage, determining a soil moisture level for the
field region, and identifying a plurality of crops to recommend
based on the determined soil moisture level; (k) determining an
expected heat unit value for the field region based on the input
data, receiving a plurality of crop options considered for
planting, wherein each of the plurality of crop options includes
crop data, determining a relative maturity for each of the
plurality of crop options based on the expected heat unit value and
the crop data, and recommending a selected crop from the plurality
of crop options based on the relative maturity for each of the
plurality of crop options; (l) receiving a plurality of pest risk
data wherein each of the plurality of pest risk data includes a
pest identifier and a pest location, receiving a plurality of crop
identifiers associated with a plurality of crops, receiving a
plurality of pest spray information associated with the crop
identifiers, determining a pest risk assessment associated with
each of the plurality of crops, and recommending a spray strategy
based on the plurality of pest risk assessments; (m) receiving a
plurality of historical agricultural activities associated with
each of the field region from a user device, and providing a
recommended field activity option based at least in part on the
plurality of historical agricultural activities; and (n) utilizing
a grid-based model to obtain localized field condition data.
[0124] As used herein, a processor may include any programmable
system including systems using micro-controllers, reduced
instruction set circuits (RISC), application specific integrated
circuits (ASICs), logic circuits, and any other circuit or
processor capable of executing the functions described herein. The
above examples are example only, and are thus not intended to limit
in any way the definition and/or meaning of the term
"processor."
[0125] As used herein, the term "database" may refer to either a
body of data, a relational database management system (RDBMS), or
to both. As used herein, a database may include any collection of
data including hierarchical databases, relational databases, flat
file databases, object-relational databases, object oriented
databases, and any other structured collection of records or data
that is stored in a computer system. The above examples are example
only, and thus are not intended to limit in any way the definition
and/or meaning of the term database. Examples of RDBMS's include,
but are not limited to including, Oracle.RTM. Database, MySQL,
IBM.RTM. DB2, Microsoft.RTM. SQL Server, Sybase.RTM., and
PostgreSQL. However, any database may be used that enables the
systems and methods described herein. (Oracle is a registered
trademark of Oracle Corporation, Redwood Shores, Calif.; IBM is a
registered trademark of International Business Machines
Corporation, Armonk, N.Y.; Microsoft is a registered trademark of
Microsoft Corporation, Redmond, Wash.; and Sybase is a registered
trademark of Sybase, Dublin, Calif.)
[0126] In one embodiment, a computer program is provided, and the
program is embodied on a computer readable medium. In an example
embodiment, the system is executed on a single computer system,
without requiring a connection to a sever computer. In a further
embodiment, the system is being run in a Windows.RTM. environment
(Windows is a registered trademark of Microsoft Corporation,
Redmond, Wash.). In yet another embodiment, the system is run on a
mainframe environment and a UNIX.RTM. server environment (UNIX is a
registered trademark of X/Open Company Limited located in Reading,
Berkshire, United Kingdom). The application is flexible and
designed to run in various different environments without
compromising any major functionality. In some embodiments, the
system includes multiple components distributed among a plurality
of computing devices. One or more components may be in the form of
computer-executable instructions embodied in a computer-readable
medium.
[0127] As used herein, an element or step recited in the singular
and proceeded with the word "a" or "an" should be understood as not
excluding plural elements or steps, unless such exclusion is
explicitly recited. Furthermore, references to "example embodiment"
or "one embodiment" of the present disclosure are not intended to
be interpreted as excluding the existence of additional embodiments
that also incorporate the recited features. As used herein, the
terms "software" and "firmware" are interchangeable, and include
any computer program stored in memory for execution by a processor,
including RAM memory, ROM memory, EPROM memory, EEPROM memory, and
non-volatile RAM (NVRAM) memory. The above memory types are example
only, and are thus not limiting as to the types of memory usable
for storage of a computer program.
[0128] The systems and processes are not limited to the specific
embodiments described herein. In addition, components of each
system and each process can be practiced independent and separate
from other components and processes described herein. Each
component and process also can be used in combination with other
assembly packages and processes.
[0129] The following detailed description illustrates embodiments
of the disclosure by way of example and not by way of limitation.
It is contemplated that the disclosure has general application to
the management and recommendation of agricultural activities.
[0130] FIG. 1 is a diagram depicting an example agricultural
environment 100 including a plurality of fields that are monitored
and managed using an agricultural intelligence computer system.
Example agricultural environment 100 includes grower 110
cultivating a plurality of fields 120 including a first field 122
and a second field 124. Grower 110 interacts with agricultural
intelligence computer system 150 to effectively manage fields 120
and receive recommendations for agricultural activities to
effectively utilize fields 120. Agricultural intelligence computer
system 150 utilizes a plurality of computer systems 112, 114, 116,
118, 130A, 130B, and 140 to provide such services. Computer systems
112, 114, 116, 118, 130A, 130B, 140, and 150 and all associated
sub-systems may be referred to as a "networked agricultural
intelligence system." Although only one grower 110 and only two
fields 120 are shown, it should be understood that multiple growers
110 having multiple fields 120 may utilize agricultural
intelligence computer system 150.
[0131] In the example embodiment, grower 110 utilizes user devices
112, 114, 116, and/or 118 to interact with agricultural
intelligence computer system 150. In one example, user device 112
is a smart watch, computer-enabled glasses, smart phone, PDA, or
"phablet" computing device capable of transmitting and receiving
information such as described herein. Alternately, grower 110 may
utilize tablet computing device 114, or laptop 116 to interact with
agricultural intelligence computer system 150. As user devices 112
and 114 are "mobile devices" with specific types and ranges of
inputs and outputs, in at least some examples user devices 112 and
114 utilize specialty software (sometimes referred to as "apps") to
interact with agricultural intelligence computer system 150.
[0132] In an example embodiment, agricultural machine 117 (e.g.,
combine, tractor, cultivator, plow, subsoiler, sprayer or other
machinery used on a farm to help with farming) may be coupled to a
computing device 118 ("agricultural machine computing device") that
interacts with agricultural intelligence computer system 150 in a
similar manner as user devices 112, 114, and 116. In some examples,
agricultural machine computing device 118 could be a planter
monitor, planter controller or a yield monitor. In some examples,
the agricultural machine computing device 118 could be a planter
monitor as disclosed in U.S. Pat. No. 8,738,243, incorporated
herein by reference, or in International Patent Application No.
PCT/US2013/054506, incorporated herein by reference. In some
examples, the agricultural machine computing device 118 could be a
yield monitor as disclosed in U.S. patent application Ser. No.
14/237,844, incorporated herein by reference. Agricultural machine
117 and agricultural machine computing device 118 may provide
agricultural intelligence computer system 150 with field definition
data 160 and field-specific data, as described below.
[0133] As described below and herein, grower (or user) 110
interacts with user devices 112, 114, 116, and/or 118 to obtain
information regarding the management of fields 120. More
specifically, grower 110 interacts with user devices 112, 114, 116,
and/or 118 in order to obtain recommendations, services, and
information related to the management of fields 120. Grower 110
provides field definition data 160 descriptive of the location,
layout, geography, and topography of fields 120 via user devices
112, 114, 116, and/or 118. In an example embodiment, grower 110 may
provide field definition data 160 to agricultural intelligence
computer system 150 by accessing a map (served by agricultural
intelligence computer system 150) on user device 112, 114, 116,
and/or 118 and selecting specific CLUs that have been graphically
shown on the map. In an alternative embodiment, grower 110 may
identify field definition data 160 by accessing a map (served by
agricultural intelligence computer system 150) on user device 112,
114, 116, and/or 118 and drawing boundaries of fields 120 (or, more
specifically, field 122 and field 124) over the map. Such CLU
selection or map drawings represent geographic identifiers. In
alternative embodiments, the user may identify field definition
data 160 by accessing field definition data 160 (provided as shape
files or in a similar format) from the U.S. Department of
Agriculture Farm Service Agency or other source via the user device
and providing such field definition data 160 to the agricultural
intelligence computer system. The land identified by "field
definition data" may be referred to as a "field" or "land tract."
As used herein, the land farmed, or "land tract", is contained in a
region that may be referred to as a "field region." Such a "field
region" may be coextensive with, for example, temperature grids or
precipitation grids, as used and defined below.
[0134] Specifically, field definition data 160 defines the location
of fields 122 and 124. As described herein, accurate locations of
fields 122 and 124 are useful in order to identify field-specific
& environmental data 170 and/or field condition data 180.
Significant variations may exist in field conditions over small
distances including variances in, for example, soil quality, soil
composition, soil moisture levels, nitrogen levels, relative
maturity of crops, precipitation, wind, temperature, solar
exposure, other meteorological conditions, and workability of the
field. As such, agricultural intelligence computer system 150
identifies a location for each of fields 122 and 124 based on field
definition data 160 and identifies a field region for each of
fields 122 and 124. As described above, in one embodiment
agricultural intelligence computer system 150 utilizes a "grid"
architectural model that subdivides land into grid sections that
are 2.5 miles by 2.5 miles in dimension.
[0135] Accordingly, agricultural intelligence computer system 150
utilizes field definition data 160 to identify which field
conditions and field data to process and determine for a particular
field. In the example, data networks 130A and 130B represent data
sources associated with fields 124 and 122, respectively, because
the grid associated with field 122 is monitored by external data
source 130B and the grid associated with field 124 is monitored by
data network 130A. Each of data networks 130A and 130B may each
have associated subsystems 131A, 132A, 133A, 134A (associated with
data network 130A) and 131B, 132B, 133B, and 134B (associated with
external data source 130B). Accordingly, field definition data 160
associates field 122 with data network 130A and field 124 with data
network 130B. Such a distinction of regions covered by an data
network 130A and 130B is provided for illustrative purposes. In
operation, data networks 130A and 130B may be associated with a
plurality of grids and be able to provide field-specific &
environmental data 170 for a particular grid based on field
definition data 160.
[0136] Data networks 130A and 130B, as described herein, receive a
plurality of information to determine field-specific &
environmental data 170. Data networks 130A and 130B may receive
feeds of meteorological data from other external services or be
associated with meteorological devices such as anemometer 135 and
rain gauge 136. Accordingly, based on such devices 135 and 136 and
other accessible data, data networks 130A and 130B provide
field-specific & environmental data 170 to agricultural
intelligence computer system 150.
[0137] Further, agricultural intelligence computer system may
receive additional information from other data networks 140 to
determine field-specific & environmental data 170 and field
condition data 180. In the example, other data networks 140 receive
inputs from aerial monitoring system 145 and satellite device 146.
Such inputs 145 and 146 may provide field-specific &
environmental data for a plurality of fields 120.
[0138] Using field-specific & environmental data 170 associated
with each field 122 and 124 (as defined by field definition data
160), agricultural intelligence computer system determines field
condition data 180 and/or at least one recommended agricultural
activity 190, as described herein. Field condition data 180
substantially represents a response to a request from grower 110
for information related to field conditions of fields 120 including
field weather conditions, field workability conditions, growth
stage conditions, soil moisture, and precipitation conditions.
Recommended agricultural activity 190 includes outputs from any of
the plurality of services described herein including planting
advisor, a nitrogen application advisor, a pest advisor, a field
health advisor, a harvest advisor, and a revenue advisor.
Accordingly, recommended agricultural activity 190 may include, for
example, suggestions on planting, nitrogen application, pest
response, field health remediation, harvesting, and sales and
marketing of crops.
[0139] Agricultural intelligence computer system 150 may be
implemented using a variety of distinct computing devices such as
agricultural intelligence computing devices 151, 152, 153, and 154
using any suitable network. In an example embodiment, agricultural
intelligence computer system 150 uses a client-server architecture
configured for exchanging data over a network (e.g., the Internet)
with other computer systems including systems 112, 114, 116, 118,
130A, 130B, and 140. One or more user devices 112, 114, 116, and/or
118 may communicate via a network using a suitable method of
interaction including a user application (or application platform)
stored on user devices 112, 114, 116, and/or 118 or using a
separate application utilizing (or calling) an application platform
interface. Other example embodiments may include other network
architectures, such as peer-to-peer or distributed network
environment.
[0140] The user application may provide server-side functionality,
via the network to one or more user devices 112, 114, 116, and/or
118. In an example embodiment, user device 112, 114, 116, and/or
118 may access the user application via a web client or a
programmatic client. User devices 112, 114, 116, and/or 118 may
transmit data to, and receive data from, from one or more front-end
servers. In an example embodiment, the data may take the form of
requests and user information input, such as field-specific data,
into the user device. One or more front-end servers may process the
user device requests and user information and determine whether the
requests are service requests or content requests, among other
things. Content requests may be transmitted to one or more content
management servers for processing. Application requests may be
transmitted to one or more application servers. In an example
embodiment, application requests may take the form of a request to
provide field condition data and/or agricultural intelligence
services for one or more fields 120.
[0141] In an example embodiment, agricultural intelligence computer
system 150 may comprise one or more servers 151, 152, 153, and 154
in communication with each other. For example, agricultural
intelligence computer system 150 may comprise front-end servers
151, application servers 152, content management servers 153,
account servers 154, modeling servers 155, environmental data
servers 156, and corresponding databases 157. As noted above,
environmental data may be obtained from data networks 130A, 130B,
and 140, accessible by agricultural intelligence computer system
150 or such environmental data may be obtained from internal data
sources or databases integrated within agricultural intelligence
computer system 150.
[0142] In an example embodiment, data networks 130A, 130B, and 140
may comprise third-party hosted servers that provide services to
agricultural intelligence computer system 150 via Application
Program Interface (API) requests and responses. The frequency at
which agricultural intelligence computer system 150 may consume
data published or made available by these third-party hosted
servers 130A, 130B, and 140 may vary based on the type of data. In
an example embodiment, a notification may be sent to the
agricultural intelligence computer system when new data is
available by a data source. Agricultural intelligence computer
system 150 may transmit an API call via the network to servers
130A, 130B, and 140 hosting the data and receive the new data in
response to the call. To the extent needed, agricultural
intelligence computer system 150 may process the data to enable
components of the agricultural intelligence computer system and
user application to handle the data. For example, processing data
may involve extracting data from a stream or a data feed and
mapping the data to a data structure, such as an XML data
structure. Data received and/or processed by agricultural
intelligence computer system 150 may be transmitted to the
application platform and stored in an appropriate database.
[0143] When an application request is made, one or more front end
servers 151 communicate with applications servers 151, content
management servers 153, account servers 154, modeling servers 155,
environmental data servers 156, and corresponding databases 157. In
one example, modeling servers 155 may generate a predetermined
number of simulations (e.g., 10,000 simulations) using, in part,
field-specific data and environmental data for one or more fields
identified based on field definition data and user information.
Depending on the type of application request, the field-specific
data and environmental data for one or more fields may be located
in content management servers 153, account servers 154,
environmental data servers 156, corresponding databases 157, and,
in some instances, archived in modeling servers 155 and/or
application servers 152. Based on the simulations generated by
modeling servers 155, field condition data and/or agricultural
intelligence services for one or more fields is provided to
application servers 152 for transmission to the requesting user
device 112, 114, 116, and/or 118 via the network. More
specifically, grower (or user) 110 may use user device 112, 114,
116, and/or 118 to access a plurality of windows or displays
showing field condition data and/or agricultural intelligence
services, as described below.
[0144] FIG. 2 is a block diagram of a user computing device 202,
used for managing and recommending agricultural activities, as
shown in the agricultural environment of FIG. 1. User computing
device 202 may include, but is not limited to, smartphone 112,
tablet 114, laptop 116, and agricultural computing device 118 (all
shown in FIG. 1). Alternately, user computing device 202 may be any
suitable device used by user 110. In the example embodiment, user
system 202 includes a processor 205 for executing instructions. In
some embodiments, executable instructions are stored in a memory
area 210. Processor 205 may include one or more processing units,
for example, a multi-core configuration. Memory area 210 is any
device allowing information such as executable instructions and/or
written works to be stored and retrieved. Memory area 210 may
include one or more computer readable media.
[0145] User system 202 also includes at least one media output
component 215 for presenting information to user 201. Media output
component 215 is any component capable of conveying information to
user 201. In some embodiments, media output component 215 includes
an output adapter such as a video adapter and/or an audio adapter.
An output adapter is operatively coupled to processor 205 and
operatively couplable to an output device such as a display device,
a liquid crystal display (LCD), organic light emitting diode (OLED)
display, or "electronic ink" display, or an audio output device, a
speaker or headphones.
[0146] In some embodiments, user system 202 includes an input
device 220 for receiving input from user 201. Input device 220 may
include, for example, a keyboard, a pointing device, a mouse, a
stylus, a touch sensitive panel, a touch pad, a touch screen, a
gyroscope, an accelerometer, a position detector, or an audio input
device. A single component such as a touch screen may function as
both an output device of media output component 215 and input
device 220. User system 202 may also include a communication
interface 225, which is communicatively couplable to a remote
device such as agricultural intelligence computer system 150.
Communication interface 225 may include, for example, a wired or
wireless network adapter or a wireless data transceiver for use
with a mobile phone network, Global System for Mobile
communications (GSM), 3G, or other mobile data network or Worldwide
Interoperability for Microwave Access (WIMAX).
[0147] Stored in memory area 210 are, for example, computer
readable instructions for providing a user interface to user 201
via media output component 215 and, optionally, receiving and
processing input from input device 220. A user interface may
include, among other possibilities, a web browser and client
application. Web browsers enable users, such as user 201, to
display and interact with media and other information typically
embedded on a web page or a website from agricultural intelligence
computer system 150. A client application allows user 201 to
interact with a server application from agricultural intelligence
computer system 150.
[0148] As described herein, user system 202 may be associated with
a variety of device characteristics. For example device
characteristics may vary in terms of the operating system used by
user device 202 in the initiating of the first transaction, the
browser operating system used by user device 202 in the initiating
of the first transaction, a plurality of hardware characteristics
associated with user device 202 in the initiating of the first
transaction, the internet protocol address associated with user
device 202 in the initiating of the first transaction, the internet
service provider associated with user device 202 in the initiating
of the first transaction, display attributes and characteristics
used by a browser used by user device 202 in the initiating of the
first transaction, configuration attributes used by a browser used
by user device 202 in the initiating of the first transaction, and
software components used by user device 202 in the initiating of
the first transaction. As further described herein, agricultural
intelligence computer system 150 (shown in FIG. 1) is capable of
receiving device characteristic data related to user system 202 and
analyzing such data as described herein.
[0149] FIG. 3 is a block diagram of a computing device, used for
managing and recommending agricultural activities, as shown in the
agricultural environment of FIG. 1. Server system 301 may include,
but is not limited to, data network systems 130A, 130B, and 140 and
agricultural intelligence computer system 150. In the example
embodiment, server system 301 determines and analyzes
characteristics of devices used in payment transactions, as
described below.
[0150] Server system 301 includes a processor 305 for executing
instructions. Instructions may be stored in a memory area 310, for
example. Processor 305 may include one or more processing units
(e.g., in a multi-core configuration) for executing instructions.
The instructions may be executed within a variety of different
operating systems on the server system 301, such as UNIX, LINUX,
Microsoft Windows.RTM., etc. It should also be appreciated that
upon initiation of a computer-based method, various instructions
may be executed during initialization. Some operations may be
required in order to perform one or more processes described
herein, while other operations may be more general and/or specific
to a particular programming language (e.g., C, C#, C++, Java,
Python, or other suitable programming languages, etc.).
[0151] Processor 305 is operatively coupled to a communication
interface 315 such that server system 301 is capable of
communicating with a remote device such as a user system or another
server system 301. For example, communication interface 315 may
receive requests from user systems 112, 114, 116, and 118 via the
Internet, as illustrated in FIGS. 2 and 3.
[0152] Processor 305 may also be operatively coupled to a storage
device 330. Storage device 330 is any computer-operated hardware
suitable for storing and/or retrieving data. In some embodiments,
storage device 330 is integrated in server system 301. For example,
server system 301 may include one or more hard disk drives as
storage device 330. In other embodiments, storage device 330 is
external to server system 301 and may be accessed by a plurality of
server systems 301. For example, storage device 330 may include
multiple storage units such as hard disks or solid state disks in a
redundant array of inexpensive disks (RAID) configuration. Storage
device 330 may include a storage area network (SAN) and/or a
network attached storage (NAS) system.
[0153] In some embodiments, processor 305 is operatively coupled to
storage device 330 via a storage interface 320. Storage interface
320 is any component capable of providing processor 305 with access
to storage device 330. Storage interface 320 may include, for
example, an Advanced Technology Attachment (ATA) adapter, a Serial
ATA (SATA) adapter, a Small Computer System Interface (SCSI)
adapter, a RAID controller, a SAN adapter, a network adapter,
and/or any component providing processor 305 with access to storage
device 330.
[0154] Memory area 310 may include, but are not limited to, random
access memory (RAM) such as dynamic RAM (DRAM) or static RAM
(SRAM), read-only memory (ROM), erasable programmable read-only
memory (EPROM), electrically erasable programmable read-only memory
(EEPROM), and non-volatile RAM (NVRAM). The above memory types are
exemplary only, and are thus not limiting as to the types of memory
usable for storage of a computer program.
[0155] FIG. 4 is an example data flowchart of managing and
recommending agricultural activities using computing devices of
FIGS. 1, 2, and 3 in the agricultural environment shown in FIG. 1.
As described herein, grower 110 uses any suitable user device 112,
114, 116, and/or 118 (shown in FIG. 1) to specify grower request
401 which is transmitted to agricultural intelligence computer
system 150. As described, grower 110 uses user application or
application platform, served on user device 114, to interact with
agricultural intelligence computer system 150 and make any suitable
grower request 401. As described herein, grower request 401 may
include a request for field condition data 180 and/or a request for
a recommended agricultural activity 190.
[0156] The application platform (or user application) may provide
server-side functionality, via the network to one or more user
devices 114. In an example embodiment, user device 114 may access
the application platform via a web client or a programmatic client.
User device 114 may transmit data to, and receive data, from one or
more front-end servers such as front end server 151 (shown in FIG.
1). In an example embodiment, the data may take the form of grower
requests 401 and user information input 402, such as field-specific
& environmental data 170 (provided by grower 110), into user
device 114. One or more front-end servers 151 may process grower
requests 401 and user information input 402 and determine whether
grower requests 401 are service requests (i.e., requests for
recommended agricultural activities 190) or content requests (i.e.,
requests for field condition data 180), among other things. Content
requests may be transmitted to one or more content management
servers 153 (shown in FIG. 1) for processing. Application requests
may be transmitted to one or more application servers 152 (shown in
FIG. 1). In an example embodiment, application requests may take
the form of a grower request 401 to provide field condition data
180 and/or agricultural intelligence services for one or more
fields 120 (shown in FIG. 1).
[0157] In an example embodiment, the application platform may
comprise one or more servers 151, 152, 153, and 154 (shown in FIG.
1) in communication with each other. For example, agricultural
intelligence computer system 150 may comprise front-end servers
151, application servers 152, content management servers 153,
account servers 154, modeling servers 155, environmental data
servers 156, and corresponding databases 157 (all shown in FIG. 1).
Further, agricultural intelligence computer system includes a
plurality of agricultural intelligence modules 158 and 159. In the
example embodiment, agricultural intelligence modules 158 and 159
are harvest advisor module 158 and revenue advisor module 159. In
further examples, planting advisor module, nitrogen application
advisor module, pest and disease advisor module, and field health
advisor module may be represented in agricultural intelligence
computer system 150. As noted above, environmental data may be
obtained from data networks 130 and 140 accessible by agricultural
intelligence computer system 150 or it may be obtained from
internal data sources integrated within agricultural intelligence
computer system 150.
[0158] In an example embodiment, data networks 130 and 140 may
comprise third-party hosted servers that provide services to
agricultural intelligence computer system 150 via Application
Program Interface (API) requests and responses. The frequency at
which agricultural intelligence computer system 150 may consume
data published or made available by these third-party hosted
servers 130 and 140 may vary based on the type of data. In an
example embodiment, a notification may be sent to agricultural
intelligence computer system 150 when new data is made available.
Agricultural intelligence computer system 150 may alternately
transmit an API call via the network to external data sources 130
hosting the data and receive the new data in response to the call.
To the extent needed, agricultural intelligence computer system 150
may process the data to enable components of the application
platform to handle the data. For example, processing data may
involve extracting data from a stream or a data feed and mapping
the data to a data structure, such as an XML data structure. Data
received and/or processed by agricultural intelligence computer
system 150 may be transmitted to the application platform and
stored in an appropriate database.
[0159] When an application request is made, one or more application
servers 152 communicate with content management servers 153,
account servers 154, modeling servers 155, environmental data
servers 156, and corresponding databases 157. In one example,
modeling servers 155 may generate a predetermined number of
simulations (e.g., 10,000 simulations) using, in part,
field-specific & environmental data 170 for one or more fields
120 identified based on field definition data 160 and user input
information 402. Depending on the type of grower request 401,
field-specific & environmental data 170 for one or more fields
120 may be located in content management servers 153, account
servers 154, modeling servers 155, environmental data servers 156,
and corresponding databases 157, and, in some instances, archived
in the application servers 152. Based on the simulations generated
by modeling servers 155, field condition data 180 and/or
agricultural intelligence services (i.e., recommended agricultural
activities 190) for one or more fields 120 is provided to
application servers 152 for transmission to requesting user device
114 via the network. More specifically, the user may use user
device 114 to access a plurality of windows or displays showing
field condition data 180 and/or recommended agricultural activities
190, as described below.
[0160] Although the aforementioned application platform has been
configured with various exemplary embodiments above, one skilled in
the art will appreciate that any configuration of servers may be
possible and that example embodiments of the present disclosure
need not be limited to the configurations disclosed herein.
[0161] In order to provide field condition data 180, agricultural
intelligence computer system 150 runs a plurality of field
condition data analysis modules 410. Field condition analysis
modules include field weather data module 411 which is configured
to determine weather conditions for each field 120 identified by
grower 110. Agricultural intelligence computer system 150 uses
field weather data module 411 to determine field temperature, wind,
humidity, and dew point. Agricultural intelligence computer system
150 also uses field weather data module 411 to determine forecasted
weather conditions including field temperature, wind, humidity, and
dew point for hourly projected intervals, daily projected
intervals, or any interval specified by grower 110. Field
precipitation module 415, field workability module 412, and field
growth stage module 413 also receive and process the forecasted
weather conditions. Near-term forecasts are determined using a
meteorological model (e.g., the Microcast model) while long-term
projections are determined using historical analog simulations.
[0162] Agricultural intelligence computer system 150 uses grid
temperatures to determine temperature values. Known research shows
that using grid techniques provides more accurate temperature
measurements than point-based temperature reporting. Temperature
grids are typically square physical regions, typically 2.5 miles by
2.5 miles. Agricultural intelligence computer system 150 associates
fields (e.g., fields 122 or 124) with a temperature grid that
contains the field. Agricultural intelligence computer system 150
identifies a plurality of weather stations that are proximate to
the temperature grid. Agricultural intelligence computer system 150
receives temperature data from the plurality of weather stations.
The temperatures reported by the plurality of weather stations are
weighted based on their relative proximity to the grid such that
more proximate weather stations have higher weights than less
proximate weather stations. Further, the relative elevation of the
temperature grid is compared to the elevation of the plurality of
weather stations. Temperature values reported by the plurality of
weather stations are adjusted in response to the relative
difference in elevation. In some examples, the temperature grid
includes or is adjacent to a body of water. Bodies of water are
known to cause a reduction in the temperature of an area.
Accordingly, when a particular field is proximate to a body of
water as compared to the weather station providing the temperature
reading, the reported temperature for the field is adjusted
downwards to account for the closer proximity to the body of
water.
[0163] Precipitation values are similarly determined using
precipitation grids that utilize meteorological radar data.
Precipitation grids have similar purposes and characteristics as
temperature grids. Specifically, agricultural intelligence computer
system 150 uses available data sources such as the National Weather
Service's NEXRAD Doppler radar data. Agricultural intelligence
computer system 150 further validates and calibrates reported data
with ground station and satellite data. In the example embodiment,
the Doppler radar data is obtained for the precipitation grid. The
Doppler radar data is used to determine an estimate of
precipitation for the precipitation grid. The estimated
precipitation is adjusted based on other data sources such as other
weather radar sources, ground weather stations (e.g., rain gauges),
satellite precipitation sources (e.g., the National Oceanic and
Atmospheric Administration's Satellite Applications and Research),
and meteorological sources. By utilizing multiple distinct data
sources, more accurate precipitation tracking may be
accomplished.
[0164] Current weather conditions and forecasted weather conditions
(hourly, daily, or as specified by the user) are displayed on the
user device graphically along with applicable information regarding
the specific field, such as field name, crop, acreage, field
precipitation, field workability, field growth stage, soil
moisture, and any other field definition data or field-specific
& environmental data 170 that the user may specify. Such
information may be displayed on the user device in one or more
combinations and level of detail as specified by the user.
[0165] In an example embodiment, temperature can be displayed as
high temperatures, average temperatures and low temperatures over
time. Temperature can be shown during a specific time and/or date
range and/or harvest year and compared against prior times, years,
including a 5 year average, a 15 year average, a 30 year average or
as specified by the user.
[0166] In an example embodiment, field precipitation module 415
determines and provides the amount of precipitation and/or
accumulated precipitation over time. Precipitation can be shown
during a specific time period and/or date range and/or harvest year
and compared against prior times, years, including a 5 year
average, a 15 year average, a 30 year average or as specified by
the user. Precipitation can also be displayed as past and future
radar data. In an example embodiment, past radar may be displayed
over the last 1.5 hours or as specified by the user. Future radar
may be displayed over the next 6 hours or as specified by the user.
Radar may be displayed as an overlay of an aerial image map showing
the user's one or more fields where the user has the ability to
zoom in and out of the map. Radar can be displayed as static at
intervals selected by the user or continuously over intervals
selected by the user. The underlying radar data received and/or
processed by the agricultural intelligence computer system may be
in the form of Gridded Binary (GRIB) files that includes forecast
reflectivity files, precipitation type, and precipitation-typed
reflectivity values.
[0167] As part of field condition data 180 provided, agricultural
intelligence computer system 150 runs or executes field workability
data module 412, which processes field-specific & environmental
data 170 and user information output 402 to determine the degree to
which a field or section of a field (associated with the field
definition data) may be worked for a given time of year using
machinery or other implements. In an example embodiment,
agricultural intelligence computer system 150 retrieves field
historical precipitation data over a predetermined period of time,
field predicted precipitation over a predetermined period of time,
and field temperatures over a predetermined period of time. The
retrieved data is used to determine one or more workability index
as determined by field workability data module 412.
[0168] In an example embodiment, the workability index may be used
to derive three values of workability for particular farm
activities. The value of "Good" workability indicates high
likelihood that field conditions are acceptable for use of
machinery or a specified activity during an upcoming time interval.
The value of "Check" workability indicates that field conditions
may not be ideal for the use of machinery or a specified activity
during an upcoming time interval. The value of "Stop" workability
indicates that field conditions are not suitable for work or a
specified activity during an upcoming time interval.
[0169] Determined values of workability may vary depending upon the
farm activity. For example, planting and tilling typically require
a low level of muddiness and may require a higher workability index
to achieve a value of "Good" than activities that allow for a
higher level of muddiness. In some embodiments, workability indices
are distinctly calculated for each activity based on a distinct set
of factors. For example, a workability index for planting may
correlate to predicted temperature over the next 60 hours while a
workability index for harvesting may be correlated to precipitation
alone. In some examples, user may be prompted at the user device to
answer questions regarding field activities if such information has
not already been provided to agricultural intelligence computer
system 150. For example, a user may be asked what field activities
are currently in use. Depending upon the response, agricultural
intelligence computer system 150 may adjust its calculations of the
workability index because of the user's activities, thereby
incorporating the feedback of the user into the calculation of the
workability index. Alternately, agricultural intelligence computer
system 150 may adjust the recommendations made to the user for
activities. In a further example, agricultural intelligence
computer system 150 may recommend that the user stop such
activities based on the responses.
[0170] As part of field condition data 180 provided, agricultural
intelligence computer system 150 runs or executes field growth
stage data module 413 (e.g., for corn, vegetative (VE-VT) and
reproductive (R1-R6) growth stages). Field growth stage data module
413 receives and processes field-specific & environmental data
170 and user information input 402 to determine timings of key
farming decisions. Agricultural intelligence computer system 150
computes crop progression for each crop through stages of growth
(agronomic stages) by tracking the impact of weather on the
phenomenological development of the crop from planting through
harvest.
[0171] In the example embodiment, agricultural intelligence
computer system 150 uses the planting date entered by the user
device. Alternately, agricultural intelligence computer system 150
may estimate the planting date using a system algorithm.
Specifically, the planting date may be estimated based on agronomic
stage data and planting practices in the region associated with the
field definition data. The planting practices may be received from
a data service such as a university data network that monitors
typical planting techniques for a region. Agricultural intelligence
computer system 150 further uses data regarding the user's farming
practices within the current season and for historical seasons,
thereby facilitating historical analysis. Agricultural intelligence
computer system 150 determines a relative maturity value of the
crops based on expected heat units over the growing season in light
of the planting date, the user's farming practices, and
field-specific & environmental data 170. As heat is a proxy for
energy received by crops, agricultural intelligence computer system
150 calculates expected heat units for crops and determines a
development of maturity of the crops.
[0172] As part of field condition data 180 provided, agricultural
intelligence computer system 150 uses and executes soil moisture
data module 414. Soil moisture data module 414 is configured to
determine the percent of total water capacity available to the crop
that is present in the soil of the field. Soil moisture data module
414 initializes output at the beginning of the growing season based
on environmental data in agricultural intelligence computer system
150 at that time, such as data from the North American Land Data
Assimilation System, and field-specific & environmental data
170.
[0173] Soil moisture values are then adjusted, at least daily,
during the growing season by tracking moisture entering the soil
via precipitation and moisture leaving the soil via
evapotranspiration (ET). Precipitation excludes a calculated amount
of water that never enters the soil because it is lost as runoff. A
runoff value is determined based on the precipitation amount over
time and a curve determined by the USDA classification of soil
type. The agricultural intelligence computer systems accounts for a
user's specific field-specific & environmental data 170 related
to soil to determine runoff and the runoff curve for the specific
field. Lighter, sandier soils allow greater precipitation water
infiltration and experience less runoff during heavy precipitation
events than heavier, more compact soils. Heavier or denser soil
types have lower precipitation infiltration rates and lose more
precipitation to runoff on days with large precipitation
events.
[0174] Daily evapotranspiration associated with a user's specific
field is calculated based on a version of the standard
Penman-Monteith ET model. The total amount of water that is
calculated as leaving the soil through evapotranspiration on a
given day is based on the following: [0175] 1. Maximum and minimum
temperatures for the day: Warmer temperatures result in greater
evapotranspiration values than cooler temperatures. [0176] 2.
Latitude: During much of the corn growing season, fields at more
northern latitudes experience greater solar radiation than fields
at more southern latitudes due to longer days. But fields at more
northern latitudes also get reduced radiation due to earth tilting.
Areas with greater net solar radiation values will have relatively
higher evapotranspiration values than areas with lower net solar
radiation values. [0177] 3. Estimated crop growth stage: Growth
stages around pollination provide the highest potential daily
evapotranspiration values while growth stages around planting and
late in grain fill result in relatively lower daily
evapotranspiration values, because the crop uses less water in
these stages of growth. [0178] 4. Current soil moisture: The
agricultural intelligence computer system's model accounts for the
fact that crops conserve and use less water when less water is
available in the soil. The reported soil moisture values reported
that are above a certain percentage, determined by crop type,
provide the highest potential evapotranspiration values and
potential evapotranspiration values decrease as soil moisture
values approach 0%. As soil moisture values fall below this
percentage, corn will start conserving water and using soil
moisture at less than optimal rates. This water conservation by the
plant increases as soil moisture values decrease, leading to lower
and lower daily evapotranspiration values. [0179] 5. Wind:
Evapotranspiration takes into account wind; however,
evapotranspiration is not as sensitive to wind as to the other
conditions. In an example embodiment, a set wind speed of 2 meters
per second is used for all evapotranspiration calculations.
[0180] Agricultural intelligence computer system 150 is
additionally configured to provide alerts based on weather and
field-related information. Specifically, the user may define a
plurality of thresholds for each of a plurality of alert
categories. When field condition data indicates that the thresholds
have been exceeded, the user device will receive alerts. Alerts may
be provided via the application (e.g., notification upon login,
push notification), email, text messages, or any other suitable
method. Alerts may be defined for crop cultivation monitoring, for
example, hail size, rainfall, overall precipitation, soil moisture,
crop scouting, wind conditions, field image, pest reports or
disease reports. Alternately, alerts may be provided for crop
growth strategy. For example, alerts may be provided based on
commodity prices, grain prices, workability indexes, growth stages,
and crop moisture content. In some examples, an alert may indicate
a recommended course of action. For example, the alert may
recommend that field activities (e.g., planting, nitrogen
application, pest and disease treatment, irrigation application,
scouting, or harvesting) occur within a particular period of time.
Agricultural intelligence computer system 150 is also configured to
receive information on farming activities from, for example, the
user device, an agricultural machine, or any other source.
Accordingly, alerts may also be provided based on logged farm
activity such as planting, nitrogen application, spraying,
irrigation, scouting, or harvesting. In some examples, alerts may
be provided regardless of thresholds to indicate certain field
conditions. In one example, a daily precipitation, growth stage,
field image or temperature alert may be provided to the user
device.
[0181] Agricultural intelligence computer system 150 is further
configured to generate a plurality of reports based on field
condition data 180. Such reports may be used by the user to improve
strategy and decision-making in farming. The reports may include
reports on crop growth stage, temperature, humidity, soil moisture,
precipitation, workability, and pest risk. The reports may also
include one or more field definition data 160, field-specific &
environmental data 170, scouting and logging events, field
condition data 180, summary of agricultural intelligence services
(e.g., recommended agricultural activities 190) or FSA Form
578.
[0182] Agricultural intelligence computer system 150 is also
configured to receive supplemental information from the user
device. For example, a user may provide logging or scouting events
regarding the fields associated with the field definition data. The
user may access a logging application at the user device and update
agricultural intelligence computer system 150. In one embodiment,
the user accesses agricultural intelligence computer system 150 via
a user device while being physically located in a field to enter
field-specific data. The agricultural intelligence computer system
might automatically display and transmit the date and time and
field definition data associated with the field-specific data, such
as geographic coordinates and boundaries. The user may provide
general data for activities including field, location, date, time,
crop, images, and notes. The user may also provide data specific to
particular activities such as planting, nitrogen application,
pesticide application, harvesting, scouting, and current weather
observations. Such supplemental information may be associated with
the other data networks and used by the user for analysis.
[0183] Agricultural intelligence computer system 150 is
additionally configured to display scouting and logging events
related to the receipt of field-specific data from the user via one
or more agricultural machines or agricultural machine devices that
interacts with agricultural intelligence computer system 150 or via
the user device. Such information can be displayed as specified by
the user. In one example, the information is displayed on a
calendar on the user device, wherein the user can obtain further
details regarding the information as necessary. In another example,
the information is displayed in a table on the user device, wherein
the user can select the specific categories of information that the
user would like displayed.
[0184] Agricultural Intelligence Modules 420
[0185] Planting Advisor Module 421
[0186] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to planting. More specifically, agricultural
intelligence computer system 150 includes a plurality of
agricultural intelligence modules 420 (or agricultural activity
modules) that may be used to determine recommended agricultural
activities 190 which are provided to grower 110. In at least some
examples, agricultural intelligence modules 420 may be similar to
agricultural intelligence modules 158 and 159 (shown in FIG. 1). In
at least some examples, planting advisor module 421 may be similar
to agricultural intelligence modules 158 and 159 (shown in FIG. 1).
Such agricultural intelligence modules 420 may be referred to as
agricultural intelligence services and may include planting advisor
module 421, nitrogen application advisor module 422, pest advisor
module 423, field health advisor module 424, harvest advisor module
425, revenue advisor module 426, variable rate suitability advisor
module 427, starter application advisor module 428, and tiling
advisor module 429. In one example embodiment, planting advisor
module 421 processes field-specific & environmental data 170
and user information input 402 to determine and provide planting
date recommendations. The recommendations are specific to the
location of the field and adapt to the current field condition
data.
[0187] In one embodiment, planting advisor module 421 receives one
or more of the following data points for each field identified by
the user (as determined from field definition data) in order to
determine and provide such planting date recommendations: [0188] 1.
A first set of data points is seed characteristic data. Seed
characteristic data may include any relevant information related to
seeds that are planted or will be planted. Seed characteristic data
may include, for example, seed company data, seed cost data, seed
population data, seed hybrid data, seed maturity level data, seed
disease resistance data, and any other suitable seed data. Seed
company data may refer to the manufacturer or provider of seeds.
Seed cost data may refer to the price of seeds for a given
quantity, weight, or volume of seeds. Seed population data may
include the amount of seeds planted (or intended to be planted) or
the density of seeds planted (or intended to be planted). Seed
hybrid data may include any information related to the biological
makeup of the seeds (i.e., which plants have been hybridized to
form a given seed.) Seed maturity level data may include, for
example, a relative maturity level of a given seed (e.g., a
comparative relative maturity ("CRM") value or a silk comparative
relative maturity ("silk CRM")), growing degree units ("GDUs")
until a given stage such as silking, mid-pollination, black layer,
or flowering, and a relative maturity level of a given seed at
physiological maturity ("Phy. CRM"). Disease resistance data may
include any information related to the resistance of seeds to
particular diseases. In the example embodiment, disease resistance
data includes data related to the resistance to Gray Leaf Spot,
Northern Leaf Blight, Anthracnose Stalk Rot, Goss's Wilt, Southern
Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight,
Southern Rust, Southern Virus Complex, Stewart's Leaf Blight, Corn
Lethal Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown
Rot. Other suitable seed data may include, for example, data
related to, grain drydown, stalk strength, root strength, stress
emergence, staygreen, drought tolerance, ear flex, test eight,
plant height, ear height, mid-season brittle stalk, plant vigor,
fungicide response, growth regulators sensitivity, pigment
inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing,
kernel texture, emergence, harvest appearance, harvest population,
seedling growth, cob color, and husk cover. [0189] 2. A second set
of data points is field-specific data related to soil composition.
Such field-specific data may include measurements of the acidity or
basicity of soil (e.g., pH levels), soil organic matter levels
("OM" levels), and cation exchange capacity levels ("CEC" levels).
[0190] 3. A third set of data points is field-specific data related
to field data. Such field-specific data may include field names and
identifiers, soil types or classifications, tilling status,
irrigation status. [0191] 4. A fourth set of data points is
field-specific data related to historical harvest data. Such
field-specific data may include crop type or classification,
harvest date, actual production history ("APH"), yield, grain
moisture, and tillage practice. [0192] 5. In some examples, users
may be prompted at the user device to provide a fifth set of data
points by answering questions regarding desired planting population
(e.g., total crop volume and total crop density for a particular
field) and/or seed cost, expected yield, and indication of risk
preference (e.g., general or specific: user is willing to risk a
specific number of bushels per acre to increase the chance of
producing a specific larger number of bushels per acre) if such
information has not already been provided to the agricultural
intelligence computer system.
[0193] Planting advisor module 421 receives and processes the sets
of data points to simulate possible yield potentials. Possible
yield potentials are calculated for various planting dates.
Planting advisor module 421 additionally utilizes additional data
to generate such simulations. The additional data may include
simulated weather between the planting data and harvesting date,
field workability, seasonal freeze risk, drought risk, heat risk,
excess moisture risk, estimated soil temperature, and/or risk
tolerance. Risk tolerance may be calculated based for a high
profit/high risk scenario, a low risk scenario, a balanced
risk/profit scenario, and a user defined scenario. Planting advisor
module 421 generates such simulations for each planting date and
displays a planting date recommendation for the user on the user
device. The recommendation includes the recommended planting date,
projected yield, relative maturity, and graphs the projected yield
against planting date. In some examples, the planting advisor
module also graphs the projected yield against the planting date
for spring freeze risk, the planting date for fall freeze risk, the
planting date for drought risk, the planting date for heat risk,
the planting date for excess moisture risk, the planting date for
estimated soil temperature, and the planting date for the various
risk tolerance levels. Planting advisor module 421 provides the
option of modeling and displaying alternative yield scenarios for
planting data and projected yield by modifying one or more data
points associated with seed characteristic data, field-specific
data, desired planting population and/or seed cost, expected yield,
and/or indication of risk preference. The alternative yield
scenarios may be displayed and graphed on the user device along
with the original recommendation.
[0194] Nitrogen Application Advisor Module 422
[0195] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to soil by using nitrogen application advisor
module 422. In at least some examples, nitrogen application advisor
module 422 may be similar to agricultural intelligence modules 158
and 159 (shown in FIG. 1). Nitrogen application advisor module 422
determines potential needs for nitrogen in the soil and recommends
nitrogen application practices to a user. More specifically,
nitrogen application advisor module 422 is configured to identify
conditions when crop needs cannot be met by nitrogen present in the
soil. In one example embodiment, nitrogen application advisor
module 422 provides recommendations for sidedressing or spraying,
such as date and rate, specific to the location of the field and
adapt to the current field condition data. In one embodiment,
nitrogen application advisor module 422 is configured to receive
one or more of the following data points for each field identified
by the user (as determined from field definition data): [0196] 1. A
first set of data points includes environmental information.
Environmental information may include information related to
weather, precipitation, meteorology, soil and crop phenology.
[0197] 2. A second set of data points includes field-specific data
related to field data. Such field-specific data may include field
names and identifiers, soil types or classifications, tilling
status, irrigation status. [0198] 3. A third set of data points
includes field-specific data related to historical harvest data.
Such field-specific data may include crop type or classification,
harvest date, actual production history ("APH"), yield, grain
moisture, and tillage practice. [0199] 4. A fourth set of data
points is field-specific data related to soil composition. Such
field-specific data may include measurements of the acidity or
basicity of soil (e.g., pH levels), soil organic matter levels
("OM" levels), and cation exchange capacity levels ("CEC" levels).
[0200] 5. A fifth set of data points is field-specific data related
to planting data. Such field-specific data may include planting
date, seed type or types, relative maturity (RM) levels of planted
seed(s), and seed population. [0201] 6. A sixth set of data points
is field-specific data related to nitrogen data. Such
field-specific data may include nitrogen application dates,
nitrogen application amounts, and nitrogen application sources.
[0202] 7. A seventh set of data points is field-specific data
related to irrigation data. Such field-specific data may include
irrigation application dates, irrigation amounts, and irrigation
sources.
[0203] Based on the sets of data points, nitrogen application
advisor module 422 determines a nitrogen application
recommendation. As described below, the recommendation includes a
list of fields with adequate nitrogen, a list of fields with
inadequate nitrogen, and a recommended nitrogen application for the
fields with inadequate nitrogen.
[0204] In some examples, users may be prompted at the user device
to answer questions regarding nitrogen application (e.g.,
side-dressing, spraying) practices and costs, such as type of
nitrogen (e.g., Anhydrous Ammonia, Urea, UAN (Urea Ammonium
Nitrate) 28%, 30% or 32%, Ammonium Nitrate, Ammonium Sulphate,
Calcium Ammonium Sulphate), nitrogen costs, latest growth stage of
crop at which nitrogen can be applied, application equipment, labor
costs, expected crop price, tillage practice (e.g., type
(conventional, no till, reduced, strip) and amount of surface of
the field that has been tilled), residue (the amount of surface of
the field covered by residue), related farming practices (e.g.,
manure application, nitrogen stabilizers, cover crops) as well as
prior crop data (e.g., crop type, harvest date, Actual Production
History (APH), yield, tillage practice), current crop data (e.g.,
planting date, seed(s) type, relative maturity (RM) of planted
seed(s), seed population), soil characteristics (pH, OM, CEC) if
such information has not already been provided to the agricultural
intelligence computer system. For certain questions, such as latest
growth stage of crop at which nitrogen can be applied, application
equipment, labor costs, the user has the option to provide a
plurality of alternative responses to that the agricultural
intelligence computer system can optimize the nitrogen application
advisor recommendation.
[0205] Using the environmental information, field-specific data,
nitrogen application practices and costs, prior crop data, current
crop data, and/or soil characteristics, nitrogen application
advisor module 422 identifies the available nitrogen in each field
and simulates possible nitrogen application practices, dates,
rates, and next date on which workability for a nitrogen
application is "Green" taking into account predicted workability
and nitrogen loss through leaching, denitrification and
volatilization. Nitrogen application advisor module 422 generates
and displays on the user device a nitrogen application
recommendation for the user. The recommendation includes: [0206] 1.
The list of fields having enough nitrogen, including for each field
the available nitrogen, last application data, and the last
nitrogen rate applied. [0207] 2. The list of fields where nitrogen
application is recommended, including for each field the available
nitrogen, recommended application practice, recommended application
dates, recommended application rate, and next data on which
workability for the nitrogen application is "Green." [0208] 3. The
recommended date of nitrogen application for each field. In some
examples the recommended date may be optimized for either yield or
return on investment. In some examples the recommended date may be
the date at which minimum predicted nitrogen levels in the field
will reach a threshold minimum value without intervening nitrogen
application. In some examples recommended dates may be excluded or
selected based upon available equipment as indicated by the user;
for example, where no equipment for applying nitrogen is available
past a given growth stage, dates are preferably recommended before
the predicted date at which that growth stage will be reached.
[0209] 4. The recommended rate of nitrogen application for each
field for each possible or recommended application date. The
recommended rate of nitrogen application may be optimized for
either yield or return on investment.
[0210] The user has the option of modeling and displaying nitrogen
lost (total and divided into losses resulting from volatilization,
denitrification, and leaching) and crop use ("uptake") of nitrogen
over a specified time period (predefined or as defined by the user)
for the recommended nitrogen application versus one or more
alternative scenarios based on a custom application practice, date
and rate entered by the user. The user has the option of modeling
and displaying estimated return on investment for the recommended
nitrogen application versus one or more alternative scenarios based
on a custom application practice, date and rate entered by the
user. The alternative nitrogen application scenarios may be
displayed and graphed on the user device along with the original
recommendation. The user has the further option of modeling and
displaying estimated yield benefit (minimum, average, and maximum)
for the recommended nitrogen application versus one or more
alternative scenarios based on a custom application practice, date
and rate entered by the user. The user has the further option of
modeling and displaying estimated available nitrogen over any time
period specified by the user for the recommended nitrogen
application versus one or more alternative scenarios based on a
custom application practice, date and rate entered by the user. The
user has the further option of running the nitrogen application
advisor (using the nitrogen application advisor) for one or more
sub-fields or management zones within a field.
[0211] Pest Advisor Module (or Pest and Disease Advisor Module)
423
[0212] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to pest and disease by using pest advisor module
423. In at least some examples, pest advisor module 423 may be
similar to agricultural intelligence modules 158 and 159 (shown in
FIG. 1). Pest advisor module 423 is configured to identify risks
posed to crops by pest damage and/or disease damage. In an example
embodiment, pest advisor module 423 identifies risks caused by the
pests that cause that the most economic damage to crops in the U.S.
Such pests include, for example, corn rootworm, corn earworm,
soybean aphid, western bean cutworm, European corn borer, armyworm,
bean leaf beetle, Japanese beetle, and twospotted spider mite. In
some examples, the pest and disease advisor provides supplemental
analysis for each pest segmented by growth stages (e.g., larval and
adult stages). Pest advisor module 423 also identifies disease
risks caused by the diseases that cause that the most economic
damage to crops in the U.S. Such diseases include, for example,
Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss's
Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose
Leaf Blight, Southern Rust, Southern Virus Complex, Stewart's Leaf
Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, Fusarium
Crown Rot. The pest advisor is also configured to recommend
scouting practices and treatment methods to respond to such pest
and disease risks. Pest advisor module 423 is also configured to
provide alerts based on observations of pests in regions proximate
to the user's fields.
[0213] In one embodiment, pest advisor module 423 may receive one
or more of the following sets of data for each field identified by
the user (as determined from field definition data): [0214] 1. A
first set of data points is environmental information.
Environmental information includes information related to weather,
precipitation, meteorology, crop phenology and pest and disease
reporting. In some examples, pest and disease reports may be
received from a third-party server or data source such as a
university or governmental reporting service. [0215] 2. A second
set of data points is seed characteristic data. Seed characteristic
data may include any relevant information related to seeds that are
planted or will be planted. Seed characteristic data may include,
for example, seed company data, seed cost data, seed population
data, seed hybrid data, seed maturity level data, seed disease
resistance data, and any other suitable seed data. Seed company
data may refer to the manufacturer or provider of seeds. Seed cost
data may refer to the price of seeds for a given quantity, weight,
or volume of seeds. Seed population data may include the amount of
seeds planted (or intended to be planted) or the density of seeds
planted (or intended to be planted). Seed hybrid data may include
any information related to the biological makeup of the seeds
(i.e., which plants have been hybridized to form a given seed.)
Seed maturity level data may include, for example, a relative
maturity level of a given seed (e.g., a comparative relative
maturity ("CRM") value or a silk comparative relative maturity
("silk CRM")), growing degree units ("GDUs") until a given stage
such as silking, mid-pollination, black layer, or flowering, and a
relative maturity level of a given seed at physiological maturity
("Phy. CRM"). Disease resistance data may include any information
related to the resistance of seeds to particular diseases. In the
example embodiment, disease resistance data includes data related
to the resistance to Gray Leaf Spot, Northern Leaf Blight,
Anthracnose Stalk Rot, Goss's Wilt, Southern Corn Leaf Blight,
Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust,
Southern Virus Complex, Stewart's Leaf Blight, Corn Lethal
Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown Rot. Other
suitable seed data may include, for example, data related to, grain
drydown, stalk strength, root strength, stress emergence,
staygreen, drought tolerance, ear flex, test eight, plant height,
ear height, mid-season brittle stalk, plant vigor, fungicide
response, growth regulators sensitivity, pigment inhibitors,
sensitivity, sulfonylureas sensitivity, harvest timing, kernel
texture, emergence, harvest appearance, harvest population,
seedling growth, cob color, and husk cover. [0216] 3. A third set
of data points is field-specific data related to planting data.
Such field-specific data may include, for example, planting dates,
seed type, relative maturity (RM) of planted seed, and seed
population. [0217] 4. A fourth set of data points is field-specific
data related to pesticide data. Such field-specific data may
include, for example, pesticide application date, pesticide product
type (specified by, e.g., EPA registration number), pesticide
formulation, pesticide usage rate, pesticide acres tested,
pesticide amount sprayed, and pesticide source.
[0218] In some examples, users may be prompted at the user device
to answer questions regarding pesticide application practices and
costs, such as type of product type, application date, formulation,
rate, acres tested, amount, source, costs, latest growth stage of
crop at which pesticide can be applied, application equipment,
labor costs, expected crop price as well as current crop data
(e.g., planting date, seed(s) type, relative maturity (RM) of
planted seed(s), seed population) if such information has not
already been provided to the agricultural intelligence computer
system. Accordingly, pest advisor module 423 receives such data
from user devices. For certain questions, such as latest growth
stage of crop at which pesticide can be applied, application
equipment, labor costs, the user has the option to provide a
plurality of alternative responses to that agricultural
intelligence computer system 150 can optimize the pest and disease
advisor recommendation.
[0219] Pest advisor module 423 is configured to receive and process
all such sets of data points and received user data and simulate
possible pesticide application practices. The simulation of
possible pesticide practices includes, dates, rates, and next date
on which workability for a pesticide application is "Green" taking
into account predicted workability. Pest advisor module 423
generates and displays on the user device a scouting and treatment
recommendation for the user. The scouting recommendation includes
daily (or as specified by the user) times to scout for specific
pests and diseases. The user has the option of displaying a
specific subset of pests and diseases as well as additional
information regarding a specific pest or disease. The treatment
recommendation includes the list of fields where a pesticide
application is recommended, including for each field the
recommended application practice, recommended application dates,
recommended application rate, and next data on which workability
for the pesticide application is "Green." The user has the option
of modeling and displaying estimated return on investment for the
recommended pesticide application versus one or more alternative
scenarios based on a custom application practice, date and rate
entered by the user. The alternative pesticide application
scenarios may be displayed and graphed on the user device along
with the original recommendation. The user has the further option
of modeling and displaying estimated yield benefit (minimum,
average, and maximum) for the recommended pesticide application
versus one or more alternative scenarios based on a custom
application practice, date and rate entered by the user.
[0220] Field Health Advisor Module 424
[0221] Agricultural intelligence computer system 150 is also
configured to provide information regarding the health and quality
of areas of fields 120. In at least some examples, field health
advisor module 424 may be similar to agricultural intelligence
modules 158 and 159 (shown in FIG. 1). Field health advisor module
424 identifies crop health quality over the course of the season
and uses such crop health determinations to recommend scouting or
investigation in areas of poor field health. More specifically,
field health advisor module 424 receives and processes field image
data to determine, identify, and provide index values of biomass
health. The index values of biomass health may range from zero
(indicating no biomass) to 1 (indicating the maximum amount of
biomass). In an example embodiment, the index value has a specific
color scheme, so that every image has a color-coded biomass health
scheme (e.g., brown areas show the areas in the field with the
lowest relative biomass health). In one embodiment, field health
advisor module 424 may receive one or more of the following data
points for each field identified by the user (as determined from
field definition data): [0222] 1. A first set of data points
includes environmental information. Such environmental information
includes information related to satellite imagery, aerial imagery,
terrestrial imagery and crop phenology. [0223] 2. A second set of
data points includes field-specific data related to field data.
Such field-specific data may include field and soil identifiers
such as field names, and soil types. [0224] 3. A third set of data
points includes field-specific data related to soil composition
data. Such field-specific data may include measurements of the
acidity or basicity of soil (e.g., pH levels), soil organic matter
levels ("OM" levels), and cation exchange capacity levels ("CEC"
levels). [0225] 4. A fourth set of data points includes
field-specific data related to planting data. Such field-specific
data may include, for example, planting dates, seed type, relative
maturity (RM) of planted seed, and seed population.
[0226] Field health advisor module 424 receives and processes all
such data points (along with field image data) to determine and
identify a crop health index for each location in each field
identified by the user each time a new field image is available. In
an example embodiment, field health advisor module 424 determines a
crop health index as a normalized difference vegetation index
("NDVI") based on at least one near-infrared ("NIR") reflectance
value and at least one visible spectrum reflectance value at each
raster location in the field. In another example embodiment, the
crop health index is a NDVI based on multispectral reflectance.
[0227] Field health advisor module 424 generates and displays on
the user device the health index map as an overlay on an aerial map
for each field identified by the user. In an example embodiment,
for each field, the field health advisor module will display field
image date, growth stage of crop at that time, soil moisture at
that time, and health index map as an overlay on an aerial map for
the field. In an example embodiment, the field image resolution is
between 5 m and 0.25 cm. The user has the option of modeling and
displaying a list of fields based on field image date and/or crop
health index (e.g., field with lowest overall health index values
to field with highest overall health index values, field with
highest overall health index values to field with lowest overall
health index values, lowest health index value variability within
field, highest health index value variability within field, or as
specified by the user). The user also has the option of modeling
and displaying a comparison of crop health index for a field over
time (e.g., side-by-side comparison, overlay comparison). In an
example embodiment, the field health advisor module provides the
user with the ability to select a location on a field to get more
information about the health index, soil type or elevation at a
particular location. In an example embodiment, the field health
advisor module provides the user with the ability to save a
selected location, the related information, and a short note so
that the user can retrieve the same information on the user device
while in the field.
[0228] Harvest Advisor Module 425
[0229] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to timing and mechanisms of harvest using harvest
advisor module 425. In at least some examples, harvest advisor
module 425 may be similar to agricultural intelligence modules 158
and 159 (shown in FIG. 1) and more specifically to harvest advisor
module 158.
[0230] Harvest advisor computing module 425 is in data
communication with agricultural intelligence computing system 150.
Agricultural intelligence computing system 150 captures and stores
field definition data 160, field-specific & environmental data
170, and field condition data 180 within its memory device. Harvest
advisor computing module 425 receives and processes field
definition data 160, field-specific & environmental data 170,
and field condition data 180 from agricultural intelligence
computing system 150 to provide (i) grain moisture value
predictions during drydown of a particular field prior to harvest,
(ii) a projected date when the particular field will reach a target
moisture value, and (iii) harvest recommendations and planning for
one or more fields. More specifically, harvest advisor computing
module 425 is configured to: (i) identify an initial date of a crop
within a field (e.g., a black layer date); (ii) identifying an
initial moisture value associated with the crop and the initial
date; (iii) identify a target harvest moisture value associated
with the crop; (iv) receive field condition data associated with
the field; (v) compute a target harvest date for the crop based at
least in part on the initial date, the initial moisture value, the
field condition data, and the target harvest moisture value,
wherein the target harvest date indicates a date at which the crop
will have a present moisture value approximately equal to the
target harvest moisture value; and (vi) display the target harvest
date for the crop to the grower for harvest planning. The target
harvest moisture value represents the value at which grower 110
desires the crop to be when harvested (e.g., at harvest date).
Thus, the harvest advisor computing module 425 assists the grower
in projecting approximately when a given field will be ready for
harvest by projecting moisture values over time, and considering
both past weather data and future weather predictions at the given
field.
[0231] Revenue Advisor Module 426
[0232] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to selling and marketing crops using revenue
advisor module 426. In at least some examples, revenue advisor
module 426 may be similar to agricultural intelligence modules 158
and 159 (shown in FIG. 1) and more specifically to revenue advisor
module 159.
[0233] Revenue advisor module 426 is in data communication with
agricultural intelligence computing system 150. Agricultural
intelligence computing system 150 captures and stores field
definition data 160, field-specific & environmental data 170,
and field condition data 180 within its memory device. Revenue
advisor module 426 receives and processes field definition data 160
and field condition data 180 from agricultural intelligence
computing system 150 to provide (i) daily yield projections at the
national, farm, and field level, (ii) current crop prices at the
national and local level, (iii) daily revenue projections at the
farm and field level, and (iv) daily profit estimates by the field,
farm, and acre. More specifically, revenue advisor module 426 is
configured to: (i) receive field condition data 180 and field
definition data 160 from agricultural intelligence computing system
150 for each field 120 of grower 110, wherein the field condition
data 180 includes growth stage conditions, field weather
conditions, soil moisture, and precipitation conditions, and
wherein field definition data includes field identifiers,
geographic identifiers, boundary identifiers, and crop identifiers;
(ii) receive cost data from grower 110, wherein cost data includes
costs related to an individual field 120 or all of the fields
associated with grower 110; (iii) receive crop pricing data from
local and national sources; (iv) process field condition data 180,
the crop pricing data, and the cost data to determine yield data,
revenue data, and profit data for each field 120 of grower 110; and
(v) output the yield data, revenue data and profit data to user
device 112, 114, 116, and/or 118. The yield data, revenue data, and
profit data relate to an individual field, and can further relate a
plurality of additional fields associated with the grower. Yield
data includes yield estimates for a high, low, and expected case
for each field and at the national level. Revenue data includes
revenue estimates based on national and local prices for each
field. Profit data includes the expected profit for each field for
the high, low, and expected cases.
[0234] Variable Rate Suitability Advisor Module 427
[0235] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to variable rate application of crop inputs using
variable rate suitability advisor module 427.
[0236] Variable rate suitability advisor module 427 is in data
communication with agricultural intelligence computing system 150.
Agricultural intelligence computing system 150 captures and stores
field definition data 160, field-specific & environmental data
170, and field condition data 180 within its memory device.
Variable rate suitability advisor module 427 receives and processes
field definition data 160 and field condition data 180 from
agricultural intelligence computing system 150 in order to generate
a variable rate suitability score for each field 120. The variable
rate suitability score preferably reflects the suitability (e.g.,
potential revenue or yield advantage) of the field for spatially
varying rate application of one or more crop inputs (e.g., variable
seed type, variable seed population, variable fertilizer
application rate). The variable rate suitability advisor module 427
preferably displays the fields 120 (e.g., in a list view or map
view) along with the variable rate suitability scores associated
with each field.
[0237] The variable rate suitability score is preferably determined
based on the statistical spatial variation of field-specific &
environmental data 170 and field condition data 180 and other data
associated with the field. For example, the variable rate
suitability score may increase with the statistical spatial
variation of the following: index value of biomass health H (e.g.,
determined as described herein); elevation E (e.g., relative to sea
level); soil type (e.g., number N of unique soil types,
distinctness Ds of unique soil types). The relative distinctness Ds
of a plurality of (e.g., two) unique soil types is preferably
determined by referencing empirical data associating pairings of
soil types with a compatibility score indicating the degree to
which the two soils can be "managed" (e.g., in the same way without
lost yield or profit potential. The compatibility score may
decrease with the amount of variation between the soils in
empirically determined characteristics such as maximum yield
potential, yield response to fertilizer application, yield
sensitivity to seed type, yield sensitivity to seed population,
water retention rate, and cation exchange capacity. It should be
appreciated that the distinctness Ds decreases with increasing
values of the compatibility score.
[0238] In one embodiment, the variable rate suitability score
S.sub.VR for each field is calculated using the relation:
S VR = k 1 c v ( H ) + k 2 ( N ) + k 3 ( A 2 + + A N A 1 + + A N )
+ k 4 c v ( E ) + k 5 ( A 1 x = 1 N D S 1 , x + A 2 x = 1 N D S 2 ,
x + + A N x = 1 N D SN , x ) ##EQU00001##
Where:
[0239] k.sub.1 . . . k.sub.3 are empirical constants which increase
with the empirical weight or importance of the associated variable
to the variable rate suitability score; [0240] N is the number of
distinct soil types in the field; [0241] A.sub.x is the area (e.g.,
in acres) of the portion of the field comprising the xth greatest
percentage of the field in terms of surface area; [0242] D.sub.SN,x
represents the relative distinctness between the soil types of the
xth and Nth areas of the field having distinct soil types, wherein
it should be appreciated that D.sub.x,x equals zero; and [0243]
c.sub.v(y) represents the coefficient of variation of the argument
y.
[0244] Referring to FIG. 31, an exemplary process 3100 is
illustrated for displaying a variable rate suitability score. At
step 3105, the module 427 preferably obtains field data including
the data described above. At step 3110, the module 427 preferably
determines an index value of biomass health for a plurality of
regions in the field (e.g., for every square inch or square foot
within the field boundary). At step 3115, the module 427 preferably
determines the number of unique soil types in the field. At step
3120, the module 427 preferably determines the distinctness of each
pairing of unique soil types in the field. At step 3125, the module
427 preferably determines the statistical variation of values in
the field including biomass health index and elevation. At step
3130, the module 427 preferably calculates a variable rate
suitability score, e.g., using the formula provided above. At step
3135, the module 427 preferably displays an association of the
variable rate suitability score for each field with that field,
e.g., in a map showing the location of each field with a graphical
indication (e.g., numerical or legend-based) of the variable rate
suitability score visually associated with (e.g., next to) the
location of the field.
[0245] In some embodiments, the variable rate suitability advisor
module 427 additionally proposes management zone boundaries for a
field 120. In some such embodiments, the management zone boundaries
are only displayed and generated if the variable rate suitability
score exceeds a threshold. The module 427 preferably generates
soil-based management zone boundaries based on soil types. In some
embodiments, soil-based management zones having soil types with a
relative distinctness Ds less than a threshold are combined in a
single management zone boundary. In some embodiments, the module
427 modifies management zone boundaries based on a set of secondary
spatial characteristics. For example, where there is variation of
one or more secondary characteristics within a single soil-based
management zone greater than a threshold (e.g., 5%), the module 427
may divide the soil-based management zone into a plurality of
modified management zones, each modified management zone having a
variation of less than the threshold value. The secondary
characteristic may comprise any of the following characteristics:
elevation; electrical conductivity; organic matter content;
presence of tilling; presence of irrigation; yield from one or more
prior seasons; reflectivity or characteristics derived from
reflectivity (e.g., NDVI, visible reflectivity, NIR, biomass health
index); and thermal emissivity. The secondary characteristic may
comprise a mathematical function (e.g., a weighted sum) of a
plurality of the preceding characteristics.
[0246] Referring to FIG. 32, an exemplary process 3200 is
illustrated for proposing management zone boundaries. At step 3205,
the module 427 preferably determines that the variable rate
suitability score exceeds threshold. At step 3210, the module 427
preferably generates soil-based management zones based on soil
types. At step 3215, the module 427 preferably modifies the
soil-based management zones based on secondary spatial
characteristics. At step 3220, the module 427 preferably displays
the modified management zones to the user. The module 427
preferably additionally enables the user to select or modify one or
more farm practice criteria (e.g., seeding rate, seed type) on a
per-management-zone basis; in some such embodiments, the module 427
prompts the user to select a management zone (e.g., on a map or
list view), then prompts the user to select or modify a farm
practice criterion (e.g., select a seeding rate of 30,000 seeds per
acre) and then apply the selected or modified farm practice
criterion to each location in the management zone (e.g., by
associating each location within the management zone in a variable
rate or variable hybrid prescription map with the selected seeding
rate or seed type).
[0247] Starter Application Advisor Module 428
[0248] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to variable rate application of crop inputs using
starter application advisor module 428.
[0249] Starter application advisor module 428 is in data
communication with agricultural intelligence computing system 150.
Agricultural intelligence computing system 150 captures and stores
field definition data 160, field-specific & environmental data
170, and field condition data 180 within its memory device. Starter
application advisor module 428 receives and processes field
definition data 160 and field condition data 180 from agricultural
intelligence computing system 150 in order to generate a starter
application recommendation.
[0250] It should be appreciated that starter fertilizer comprises a
fertilizer applied during planting operations. Starter fertilizer
may comprise nutrients including nitrogen, phosphorous, potassium
and zinc.
[0251] The starter application recommendation preferably comprises
an amount of starter fertilizer to apply during planting
operations. The starter application recommendation may comprise a
recommended amount (e.g., pounds per acre) of starter to apply. In
some embodiments, the recommended amount of starter to apply may
vary spatially based on spatially varying data; in such
embodiments, the starter recommendation preferably comprises a
variable rate starter application map.
[0252] The starter application recommendation may be based on
starter application criteria indicated by the user or determined by
another advisor module. The starter application criteria may
include a planting date, starter application equipment criteria
(e.g., lateral distance of starter application from the seed
trench), starter fertilizer type, starter fertilizer composition
(e.g., percentage by weight of nitrogen, phosphorous, potassium,
and/or zinc). Where the starter recommendation is based on the
planting date, the recommended amount of starter is preferably
greater for earlier planting dates. Referring to FIG. 33, in some
embodiments the recommended amount of starter is selected as the
minimum amount of starter that will provide sufficient nutrients to
the seed throughout the remaining nutrient immobilization period Pi
(e.g., a time during which the microorganism population grows)
between the planting date Dp and the beginning of a microorganism
mineralization period Pm (e.g., production of available nitrates
resulting from microorganism death).
[0253] In some embodiments, the recommended starter application
(e.g., the minimum amount of starter that will provide sufficient
nutrients to the seed throughout the remaining nutrient
immobilization period) is based on the carbon load present in the
soil on the planting date. In some embodiments, the recommended
amount of starter increases with increasing estimated carbon load
and decreases with decreasing estimated carbon load. The carbon
load is preferably estimated based on soil data (e.g., soil type,
cation exchange capacity, estimated available nitrogen), farming
practices (e.g., prior crop planted, population of prior crop
planted, type of tillage applied to prior crop planted,
aggressiveness of prior crop residue processing including combine
setup criteria such as header type, timing and amount of fertilizer
applied prior to planting) and/or yield data (e.g., the
spatially-varying yield obtained from the prior crop throughout the
field). In some examples, the estimated carbon load for each
location in the field increases with the amount of yield obtained
from the prior crop at that location.
[0254] In some embodiments, a range of starter application
recommendations corresponding to a range of planting dates are
displayed to the user. In some such embodiments, a range of starter
costs (e.g., the estimated cost of the recommended amount of
starter) corresponding to the range of planting dates are also
displayed to the user. In some embodiments, the starter cost for
each planting date is used by the planting advisor module 421 in
recommending optimal planting dates.
[0255] In some embodiments, a reflectivity (e.g., infrared,
near-infrared, thermal, visual) measurement is used to adjust the
starter recommendation. In such embodiments, each location in the
field is preferably assigned an estimated residue value (e.g.,
percentage of area covered by residue) using an empirical
relationship between reflectivity and residue value. The estimated
carbon load is then adjusted based on the estimated residue value;
for example, a higher residue value may result in an increased
estimated carbon load. In some such embodiments, an aerial image of
the field is used to estimate a residue value for each location in
the field. In other embodiments, the signal from one or more
reflectivity sensors mounted to an implement drawn through the
field (e.g., a row unit of the planter) is used to estimate the
residue value.
[0256] In some embodiments, the recommended starter application is
based in part on weather data, e.g., recorded weather data prior to
the planting date and predicted weather after the planting date. In
some such embodiments, the amount of starter recommended increases
with a decreasing length of spring warm-up period (e.g., the number
of calendar days between January 1 and the first date on which the
local air temperature reaches or is predicted to reach a threshold
temperature such as 50 degrees Fahrenheit, the number of calendar
days between the planting date and the first date on which the
local air temperature reaches or is predicted to reach a threshold
temperature such as 50 degrees Fahrenheit, the number of calendar
days between January 1 and the first date on which the five-day
average local air temperature reaches or is predicted to reach a
threshold temperature such as 50 degrees Fahrenheit, the number of
calendar days between the planting date and the first date on which
the five-day average local air temperature reaches or is predicted
to reach a threshold temperature such as 50 degrees Fahrenheit). In
other embodiments, the starter recommendation is based on other
weather data including past and/or predicted precipitation and
cloud cover.
[0257] An exemplary process 3400 for generating and carrying out a
starter recommendation is illustrated in FIG. 34. At step 3405, the
nitrogen advisor module 428 preferably obtains the data described
above, either from a memory of the agricultural intelligence
computing system 150, from a third-party server or by prompting the
user to enter or select certain criteria (e.g., the starter
application criteria). At step 3410, the nitrogen advisor module
preferably estimates the carbon load at each location, preferably
based on one or more of the following as described above: soil
data, farming practices, yield data, and reflectivity data. At step
3415, the nitrogen advisor module generates a starter application
recommendation (e.g., a map associating recommended starter
application amounts with each location in the field), preferably
based on one or more of the following as described above: planting
date, carbon load, starter application criteria, and weather data.
At step 3420, starter is applied in the field according to the
starter application recommendation. In carrying out step 3420, the
nitrogen advisor module 428 may command and/or recommend a pump
rate to a variable-rate pump or a valve state to an electrically
operated liquid control valve such that starter is applied
according to the starter application recommendation. In such
embodiments, the variable-rate pump and/or liquid control valve are
preferably in data communication with the agricultural intelligence
computing system 150.
[0258] Water Management Advisor Module 429
[0259] Agricultural intelligence computer system 150 is
additionally configured to provide agricultural intelligence
services related to variable rate application of crop inputs using
water management advisor module 429.
[0260] Water management advisor module 429 is in data communication
with agricultural intelligence computing system 150. Agricultural
intelligence computing system 150 captures and stores field
definition data 160, field-specific & environmental data 170,
and field condition data 180 within its memory device. Water
management advisor module 429 receives and processes field
definition data 160 and field condition data 180 from agricultural
intelligence computing system 150 in order to generate one or more
water management recommendations.
[0261] In some embodiments, the water management advisor module 429
estimates a water management economic loss (e.g., yield loss) due
to excess water and/or insufficient water. In some such
embodiments, the water management advisor module 429 additionally
estimates a monetary economic loss by applying a multiplier (e.g.,
a commodity price obtained from a third-party commodity price
service) to the estimated water management yield loss. It should be
appreciated that water management economic loss as described herein
preferably corresponds to an amount of economic loss associated
with unmanaged or insufficiently managed water, e.g., "ponding" of
water in a region of a field or insufficient water in a region of a
field.
[0262] In some embodiments, a water management yield loss is
estimated by estimating a predicted or past-season yield loss due
to excess water (e.g., "ponding" of standing water above the soil
level) in a portion of the field. Referring to FIG. 35, a water
management map 3500 is illustrated which may be used by the water
management advisor module 429 to estimate yield loss and/or
displayed to the user along with a legend 3540 relating color or
patterns to data sets in the map. The water management map
preferably includes yield loss regions 3510; each water loss region
3510 preferably corresponds to a region in which the maximum (or in
some embodiments, the average) yield is less than a threshold
percentage (e.g., 70%) of the field average yield. The water
management map preferably includes low elevation regions 3520; each
low elevation region 3520 preferably corresponds to a region in
which the maximum (or in some embodiments, the average) elevation
is lower than a threshold percentage (e.g., 70%) of the field
average elevation. The water management advisor module 429
preferably identified one or more overlap regions 3530 in which low
elevation and yield loss regions intersect. The water management
advisor module 429 preferably determines a numerical aggregate
water management yield loss 3550 which is preferably displayed to
the user. The water management yield loss 3550 may be determined
using the following relation:
Water Management Yield
Loss=z.sub.1(Y.sub.ave,field-Y.sub.ave,overlap)
[0263] Where: [0264] z.sub.1=an empirical multiplier (e.g., a value
between 0.1 and 0.9 such as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
or 0.9); [0265] Y.sub.ave,field=the average yield for the field;
and [0266] Y.sub.ave,field=the average yield for the overlap
regions.
[0267] In some embodiments, the water management advisor module 429
estimates the potential return on investment (e.g., in terms of
yield or economic value) corresponding to a water management action
(e.g., the installation, irrigation). In some embodiments, the
estimated return on investment may comprise the water management
yield loss. In other embodiments, the estimated return on
investment may comprise the water management yield loss less an
estimated cost of the water management action. The estimated cost
may be based on cost information entered by the user or obtained
from memory or from a third-party server; in some embodiments, the
cost information may be a per-acre value and the estimated cost may
be determined by multiplying the per-acre cost information by an
estimated area of the region requiring water management (e.g., the
area of the overlap). some embodiments, the estimated return on
investment may comprise the water management yield loss (or yield
loss less estimated cost) adjusted by a multiplier representing the
ratio of estimated yield for the field to the recorded yield for
the prior season. In some embodiments, the estimated return on
investment may comprise the water management yield loss adjusted
based on the difference between weather (e.g., total rainfall) for
the past season and the current or approaching season. In exemplary
embodiments, the estimated return on investment for a
ponding-reducing action (e.g., tiling) is reduced (e.g.,
geometrically or arithmetically) as total predicted rainfall
decreases.
[0268] Turning to FIG. 36, an exemplary process for recommending
water management is illustrated. At step 3605, the water management
advisor module 429 preferably obtains the data referenced above
(e.g., yield data, elevation data, weather data) either from a
memory of the agricultural intelligence computing system 150, from
a third-party server, or by prompting the user to enter or select
certain criteria. At step 3610, the water management advisor module
429 preferably estimates a water management economic loss as
described above. At step 3615, the water management advisor module
429 preferably generates water management economic loss map as
described above and preferably displays the map to the user. At
step 3620, the water management advisor module 429 preferably
estimates a future water management economic loss (and/or return on
investment associated with a water management action such as
tiling) based on a predictive weather model (e.g., based on
predicted precipitation in one or more subsequent growing seasons
in comparison with recorded precipitation for a prior season for
which economic loss has been determined). In some embodiments, the
module generates a range of economic loss and/or return on
investment values corresponding to a range of predicted weather
(e.g., a range of predicted precipitation for a future growing
season). At step 3625, the water management advisor module 429
preferably recommends a water management action as described above.
The recommended water management action may comprise one of
drainage (e.g., tiling), irrigation (e.g., pivot, drip-tape), or no
action. The recommended water management action may include a
region of the field to which the action should be applied (e.g.,
the overlap region of FIG. 35) or an orthogonal direction in which
the action (e.g., tiling) should be applied. In some embodiments,
the recommended water management action is "no action" unless the
estimated return on investment is greater than a threshold value
(e.g., 0 dollars or 0 bushels), in which case the action (e.g.,
tiling) having greater than the threshold value is recommended. In
other embodiments, the recommended water management action is "no
action" unless the estimated return on investment based on a range
of predicted weather corresponding to a statistical confidence
value (e.g., 90%) is greater than a threshold value (e.g., 0
dollars or 0 bushels), in which case the action (e.g., tiling)
having greater than the threshold value is recommended. At step
3630, the water management recommendation is preferably
implemented. The implementation step 3630 may be carried out by the
user; in some embodiments, the implementation step 3630 may be
carried out by the module 429 and may include sending a service
communication (e.g., a service order or quote request) to a water
management service identifying recommendation criteria (e.g., field
location, the user, the yield loss region, the overlap region, the
next planting date as identified by the planting advisor or the
user).
[0269] Turning to FIG. 37, an exemplary process 3700 for estimating
a water management economic loss (e.g., in carrying out step 3610
of process 3600) is illustrated. At step 3705, the water management
advisor module 429 preferably obtains data for use in estimating an
economic loss, e.g., yield loss and elevation data. The module 429
may also obtain crop type and commodity price data. At step 3710,
the water management advisor module 429 preferably identifies yield
loss regions as described above. It should be appreciated that the
yield loss regions may be determined using a yield map generated by
harvesting the field using a combine having a yield monitor such as
one of the embodiments disclosed in U.S. patent application Ser.
No. 14/237,844 (Pub. No. 2014/0174199), hereby incorporated herein
in its entirety by reference. At step 3715, the water management
advisor module 429 preferably identifies extreme (e.g., low and/or
high) elevation regions, preferably as described above. It should
be appreciated that the extreme elevations may be determined using
an elevation map stored in memory or created by recording
GPS-reported elevation while traversing the field. At step 3720,
the water management advisor module 429 preferably identifies one
or more regions in which extreme elevation is associated (e.g.,
causally, statistically, empirically) with yield loss. At step
3725, the water management advisor module 429 preferably aggregates
the lost yield (e.g., number of bushels yielded below a field
average or other threshold) within the water management economic
loss regions. At step 3730, the water management advisor module 429
may optionally convert the lost yield value from a yield value to a
currency (e.g., dollar) value based on a crop type and an estimated
or known commodity price associated with the crop type.
[0270] Agricultural Activity Management
[0271] FIG. 5 is an example method for managing agricultural
activities in agricultural environment 100 (shown in FIG. 1).
Method 500 is implemented by agricultural intelligence computer
system 150 (shown in FIG. 1). Agricultural intelligence computer
system 150 receives 510 a plurality of field definition data.
Agricultural intelligence computer system 150 retrieves 520 a
plurality of input data from a plurality of data networks 130A,
130B, and 140. Agricultural intelligence computer system 150
determines 530 a field region based on the field definition data.
Agricultural intelligence computer system 150 identifies 540 a
subset of the plurality of input data associated with the field
region. Agricultural intelligence computer system 150 determines
550 a plurality of field condition data based on the subset of the
plurality of input data. Agricultural intelligence computer system
150 provides 560 the plurality of field condition data to the user
device.
[0272] FIG. 6 is an example method for recommending agricultural
activities in the agricultural environment of FIG. 1. Method 500 is
implemented by agricultural intelligence computer system 150 (shown
in FIG. 1). Agricultural intelligence computer system 150 receives
610 a plurality of field definition data. Agricultural intelligence
computer system 150 retrieves 620 a plurality of input data from a
plurality of data networks 130A, 130B, and 140. Agricultural
intelligence computer system 150 determines 630 a field region
based on the field definition data. Agricultural intelligence
computer system 150 identifies 640 a subset of the plurality of
input data associated with the field region. Agricultural
intelligence computer system 150 determines 650 a plurality of
field condition data based on the subset of the plurality of input
data. Agricultural intelligence computer system 150 provides 660
the plurality of field condition data to the user device.
Agricultural intelligence computer system 150 determines 670 a
recommendation score for each of the plurality of field activity
options based at least in part on the plurality of field condition
data. Agricultural intelligence computer system 150 provides 680 a
recommended field activity option from the plurality of field
activity options based on the plurality of recommendation
scores.
[0273] FIG. 7 is a diagram of components of one or more example
computing devices that may be used in the environment shown in FIG.
5. FIG. 7 further shows a configuration of databases including at
least database 157 (shown in FIG. 1). Database 157 is coupled to
several separate components within fraud detection computer system
150, which perform specific tasks.
[0274] Agricultural intelligence computer system 150 includes a
first receiving component 701 for receiving a plurality of field
definition data, a first retrieving component 702 for retrieving a
plurality of input data from a plurality of data networks, a first
determining component 703 for determining a field region based on
the field definition data, a first identifying component 704 for
identifying a subset of the plurality of input data associated with
the field region, a second determining component 705 for
determining a plurality of field condition data based on the subset
of the plurality of input data, a first providing component 706 for
providing the plurality of field condition data to the user device,
a third determining component 707 for determining a recommendation
score for each of the plurality of field activity options based at
least in part on the plurality of field condition data, and a
second providing component 708 for providing a recommended field
activity option from the plurality of field activity options based
on the plurality of recommendation scores.
[0275] In an example embodiment, database 157 is divided into a
plurality of sections, including but not limited to, a
meteorological analysis section 710, a soil and crop analysis
section 712, and a market analysis section 714. These sections
within database 157 are interconnected to update and retrieve the
information as required
[0276] FIGS. 8-30 are example illustrations of information provided
by the agricultural intelligence computer system of FIG. 3 to the
user device of FIG. 2 to facilitate the management and
recommendation of agricultural activities.
[0277] Referring to FIG. 8, screenshot 800 illustrates a setup
screen wherein grower 110 (shown in FIG. 1) may provide user
information input 402 (shown in FIG. 4) to define basic attributes
associated with their account.
[0278] Referring to FIGS. 9-11, screenshots 900, 1000, and 1100
illustrate options allowing for grower 110 (shown in FIG. 1) to
view field condition data 180 (shown in FIG. 1). As is indicated in
screenshot 900, grower 110 may select particular dates for field
condition data 180 viewing that may be in the past, present, or
future and may accordingly provide historic, current, or forecasted
field condition data 180. Grower 110 may accordingly select a
particular date and time to view field condition data 180 for
particular fields 120 (shown in FIG. 1). Screenshot 1000
illustrates a consolidated view of field condition data 180 for a
particular field 120 at a particular date. More specifically, field
condition data 180 shown includes output of field weather data
module 411, field workability data module 412, growth stage data
module 413, and soil moisture data module 414. Screenshot 1100
similarly shows output of field precipitation module 415 of a
particular field 120 over a particular time period. As described
above and herein, such field condition data 180 is determined using
a localized method that determines such field conditions uniquely
for each field 120.
[0279] FIGS. 12 and 13 illustrate such field condition data 180
displayed graphically using maps. More specifically, from the view
of screenshots 1200, grower 110 may select a particular portion of
a map to identify field condition data 180 for each of fields 120.
Screenshot 1300 accordingly illustrates such a display of field
condition data 180 for a particular field 122.
[0280] Referring to FIGS. 14-20, screenshots 1400, 1500, 1600,
1700, 1800, 1900, and 2000 illustrate the display of fields 120
(shown in FIG. 1) associated with grower 110 (shown in FIG. 1).
More specifically, in screenshot 1400 grower 110 provides field
definition data 160 (shown in FIG. 1) to define fields 120,
indicated graphically. Accordingly, a plurality of fields 120 are
illustrated and may be reviewed individually or in any combination
to obtain field condition data 180 (shown in FIG. 1) and/or
recommended agricultural activities 190 (shown in FIG. 1). Note
that screenshot 1400 illustrates that grower 110 may own, use, or
otherwise manage a plurality of fields 120 that are substantially
far from one another and associated with unique geographic and
meteorological conditions. It will be appreciated that the systems
and methods described herein, providing hyper localized field
condition data 180 and recommended agricultural activities 190,
substantially helps grower 110 to identify meaningful distinctions
between each of fields 120 in order to effectively manage each
field 120.
[0281] In screenshot 1500, grower 110 (shown in FIG. 1) may see a
tabular view indicating identifiers for each field 120 (shown in
FIG. 1) in conjunction with a map view of such fields. Grower 110
may navigate using the tabular view (or the graphical view) to
individual actions associated with each field 120. Accordingly,
screenshot 1600 illustrates enhanced information shown to grower
110 upon selecting a particular field for review from either the
tabular view or the graphical view (e.g., by clicking on one of the
fields). As is illustrated in screenshots 1700, 1800, 1900, and
2000, grower 110 may additionally enhance display (or "zoom in") to
view a smaller subset of fields 120.
[0282] Referring to FIGS. 21 and 22, screenshots 2100 and 2200
illustrate historical data that may be provided by grower 110
(shown in FIG. 1) or any other source to identify notes or details
associated with planting. More specifically, grower 110 may
navigate to a particular date in screenshot 2400 and view planting
notes as displayed in screenshot 2200.
[0283] Referring to FIG. 23, screenshot 2300 presents a tabular
view that allows grower 110 (shown in FIG. 1) to group or
consolidate common land units ("CLUs") into "field groups". As a
result, data associated with a particular field group may be viewed
commonly. In some examples, grower 110 may be interested in viewing
and managing particular fields 120 (shown in FIG. 1) in particular
combinations based on, for example, common crops or geographies.
Accordingly, the application and systems described facilitate such
effective management.
[0284] Referring to FIGS. 24-30, screenshots 2400, 2500, 2600,
2700, 2800, 2900, and 3000 illustrate the use of a "field manager"
tool that enables grower 110 (shown in FIG. 1) to view information
for a plurality of fields in a tabular format. Screenshots 2400,
2500, 2600, 2700, 2800, 2900, and 3000 further indicate that grower
110 may view field condition data 180 in common with field-specific
& environmental data 170 (shown in FIG. 1). For example
screenshot 2400 illustrates, on a per field basis, current
cultivated crop, acreage, average yield, tilling practices or
methods, and residue levels. By contrast, screenshot 2500
illustrates that grower 110 may apply a filter 2510 to identify
particular subgroups of fields 120 for review based on
characteristics including current cultivated crop, acreage, average
yield, tilling practices or methods, and residue levels. The field
manager tool also enables grower 110 to update or edit information.
Screenshots 2600, 2700, 2800, 2900, and 3000 show views wherein
grower 110 may update or edit information for previous periods of
cultivation. More specifically, in screenshot 2600, general data
may be updated while in screenshot 2700, planting data may be
updated. Similarly, in screenshot 2800, harvest data may be updated
and in screenshot 2900, nitrogen data may be updated. In screenshot
3000, soil characteristics data may be updated.
[0285] As used herein, the term "non-transitory computer-readable
media" is intended to be representative of any tangible
computer-based device implemented in any method or technology for
short-term and long-term storage of information, such as,
computer-readable instructions, data structures, program modules
and sub-modules, or other data in any device. Therefore, the
methods described herein may be encoded as executable instructions
embodied in a tangible, non-transitory, computer readable medium,
including, without limitation, a storage device and/or a memory
device. Such instructions, when executed by a processor, cause the
processor to perform at least a portion of the methods described
herein. Moreover, as used herein, the term "non-transitory
computer-readable media" includes all tangible, computer-readable
media, including, without limitation, non-transitory computer
storage devices, including, without limitation, volatile and
nonvolatile media, and removable and non-removable media such as a
firmware, physical and virtual storage, CD-ROMs, DVDs, and any
other digital source such as a network or the Internet, as well as
yet to be developed digital means, with the sole exception being a
transitory, propagating signal.
[0286] This written description uses examples to disclose the
disclosure, including the best mode, and also to enable any person
skilled in the art to practice the embodiments, including making
and using any devices or systems and performing any incorporated
methods. The patentable scope of the disclosure is defined by the
claims, and may include other examples that occur to those skilled
in the art. Such other examples are intended to be within the scope
of the claims if they have structural elements that do not differ
from the literal language of the claims, or if they include
equivalent structural elements with insubstantial differences from
the literal languages of the claims.
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