U.S. patent application number 16/933239 was filed with the patent office on 2020-12-31 for delineating management zones based on historical yield maps.
The applicant listed for this patent is THE CLIMATE CORPORATION. Invention is credited to ANAHITA HASSANZADEH, LIJUAN XU.
Application Number | 20200410143 16/933239 |
Document ID | / |
Family ID | 1000005086689 |
Filed Date | 2020-12-31 |
United States Patent
Application |
20200410143 |
Kind Code |
A1 |
XU; LIJUAN ; et al. |
December 31, 2020 |
DELINEATING MANAGEMENT ZONES BASED ON HISTORICAL YIELD MAPS
Abstract
In an embodiment, a method comprises: receiving digital yield
data representing yields of crops that have been harvested from an
agricultural field; applying an empirical cumulative density
function to the digital yield data to generate transformed digital
yield data; smoothing the transformed digital yield data to result
in generating and storing smooth transformed digital yield data;
determining a first count value for a plurality of management
classes; generating a plurality of first management zones for the
agricultural field by clustering the smooth transformed digital
yield data and using the first count value; generating a set of
first merged management zones by merging one or more small
management zones, of the plurality of first management zones, with
their respective similar neighboring large zones; storing the set
of first merged management zones and the first count value in a set
of management zone metrics.
Inventors: |
XU; LIJUAN; (San Francisco,
CA) ; HASSANZADEH; ANAHITA; (San Francisco,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
THE CLIMATE CORPORATION |
San Francisco |
CA |
US |
|
|
Family ID: |
1000005086689 |
Appl. No.: |
16/933239 |
Filed: |
July 20, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15234943 |
Aug 11, 2016 |
10719638 |
|
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16933239 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 30/20 20200101;
G06Q 50/02 20130101; G06Q 10/10 20130101; G06Q 10/063 20130101;
G06F 7/02 20130101 |
International
Class: |
G06F 30/20 20060101
G06F030/20; G06Q 50/02 20060101 G06Q050/02; G06Q 10/06 20060101
G06Q010/06; G06Q 10/10 20060101 G06Q010/10; G06F 7/02 20060101
G06F007/02 |
Claims
1. A method comprising: using instructions programmed in a computer
system comprising one or more processors and computer memory,
receiving digital yield data representing yields of crops that have
been harvested from an agricultural field; using the instructions
programmed in the computer system, transforming the digital yield
data, by applying an empirical cumulative density function to the
digital yield data, to generate transformed digital yield data;
based on the transformed digital yield data, using the instructions
programmed in the computer system, smoothing the transformed
digital yield data to result in generating and storing smooth
transformed digital yield data; using the instructions programmed
in the computer system, determining a first count value of a
plurality of management classes, wherein the plurality of
management classes includes areas in the agricultural field that
have relatively homogeneous yield limiting factors, but are not
restricted to be spatially contiguous; using the instructions
programmed in the computer system, generating a plurality of first
management zones for the agricultural field by clustering the
smooth transformed digital yield data and using the first count
value; using the instructions programmed in the computer system,
generating a set of first merged management zones by merging one or
more small management zones, of the plurality of first management
zones, with their respective similar neighboring large zones; using
the instructions programmed in the computer system, storing the set
of first merged management zones and the first count value in a set
of management zone metrics.
2. The method of claim 1, further comprising: determining a second
count value for the plurality of management classes; generating a
plurality of second management zones by clustering the smooth
transformed digital yield data and using the second count value;
generating a set of second merged management zones by merging one
or more small management zones, of the plurality of second
management zones, with their respective similar neighboring large
zones; storing the set of second merged management zones and the
second count value in the set of management zone metrics.
3. The method of claim 2, further comprising: generating a
management zone recommendation based on the set of management zone
metrics by evaluating a delineation quality of management zones
stored in the set of management zone metrics.
4. The method of claim 3, wherein the digital yield data comprises
data representing yield information collected for a multi-year time
period, and wherein the method further comprises determining a
recommended class count for the plurality of management classes
based on the set of management zone metrics.
5. The method of claim 3, wherein the digital yield data comprises
data representing yield information collected for one year, and
wherein the method further comprises determining a recommended
class count for the plurality of management classes based on the
set of management zone metrics.
6. The method of claim 1, further comprising obtaining the digital
yield data from a plurality of historical yield maps.
7. The method of claim 1, further comprising preprocessing the
digital yield data by performing one or more of: removing yield
maps that correspond to multiple crops planted in the same season
in the agricultural field, removing yield maps that include yield
records outside boundaries of the agricultural field, marking yield
records of absolute zeros as missing values, removing yield records
for fields that have less than two years of yield maps period, or
removing yield maps that have more than a certain count of missing
values.
8. The method of claim 1, further comprising using the empirical
cumulative density function to transform the digital yield data
into the transformed digital yield data including transformed
digital yield data records, each of which falls in a particular
range.
9. The method of claim 1, further comprising generating the smooth
transformed digital yield data by performing one or more of:
removing outliers data from the transformed digital yield data,
determining one or more missing values in the transformed digital
yield data and including the one or more missing values in the
smooth transformed digital yield data, or performing a spatial
smoothing on the transformed digital yield data.
10. The method of claim 1, wherein the set of first merged
management zones includes contiguous subregions within the
agricultural field that have similar yield limiting factors and are
uniformly managed; wherein the first count value for the plurality
of management classes indicates a count of management classes in
the plurality of management classes.
11. The method of claim 1, further comprising applying any one of:
a K-means approach, a fuzzy C-means approach, or a region merging
approach.
12. The method of claim 1, further comprising generating the set of
first merged management zones by executing computer instructions to
perform one or more of: applying hierarchical agglomeration;
evaluating a delineation quality of management zones by applying
one or more clustering validation measures to the set of management
zone metrics.
13. The method of claim 1, further comprising using the set of
first merged management zones to automatically control a computer
control system of one or more of seeding, irrigation, nitrogen
application, and harvesting apparatus.
14. A data processing system comprising: a memory; one or more
processors coupled to the memory and programmed to: receive digital
yield data representing yields of crops that have been harvested
from an agricultural field; transform the digital yield data, by
applying an empirical cumulative density function to the digital
yield data, to generate transformed digital yield data; based on
the transformed digital yield data, smooth the transformed digital
yield data to result in generating and storing smooth transformed
digital yield data; determine a first count value of a plurality of
management classes, wherein the plurality of management classes
includes areas in the agricultural field that have relatively
homogeneous yield limiting factors, but are not restricted to be
spatially contiguous; generate a plurality of first management
zones for the agricultural field by clustering the smooth
transformed digital yield data and using the first count value;
generate a set of first merged management zones by merging one or
more small management zones, of the plurality of first management
zones, with their respective similar neighboring large zones; store
the set of first merged management zones and the first count value
in a set of management zone metrics.
15. The data processing system of claim 14, wherein the one or more
processors are programmed to: determine a second count value for
the plurality of management classes; generate a plurality of second
management zones by clustering the smooth transformed digital yield
data and using the second count value; generate a set of second
merged management zones by merging one or more small management
zones, of the plurality of second management zones, with their
respective similar neighboring large zones; store the set of second
merged management zones and the second count value in the set of
management zone metrics.
16. The data processing system of claim 15, wherein the one or more
processors are programmed to: generate a management zone
recommendation based on the set of management zone metrics by
evaluating a delineation quality of management zones stored in the
set of management zone metrics.
17. The data processing system of claim 14, wherein the digital
yield data representing crop yields harvested from the agricultural
field is obtained from a plurality of historical yield maps.
18. The data processing system of claim 14, wherein the digital
yield data representing crop yields harvested from the agricultural
field is preprocessed by performing one or more of: removing yield
maps that correspond to multiple crops planted in the same season
in the agricultural field, removing yield maps that include yield
records outside boundaries of the agricultural field, marking yield
records of absolute zeros as missing values, removing yield records
for fields that have less than two years of yield maps period, or
removing yield maps that have more than a certain count of missing
values.
19. The data processing system of claim 14, wherein the empirical
cumulative density function transforms the digital yield data to
the transformed digital yield data including transformed digital
yield data records, each of which falls in a particular range.
20. The data processing system of claim 14, wherein the smooth
transformed digital yield data is generated by performing one or
more of: removing outliers data from the transformed digital yield
data, determining one or more missing values in the transformed
digital yield data and including the one or more missing values in
the smooth transformed digital yield data, or performing a spatial
smoothing on the transformed digital yield data.
Description
BENEFIT CLAIM
[0001] This application claims the benefit under 35 U.S.C. .sctn.
120 as a Continuation of application Ser. No. 15/234,943, filed
Aug. 11, 2016, the entire contents of which is hereby incorporated
by reference for all purposes as if fully set forth herein. The
applicants hereby rescind any disclaimer of claim scope in the
parent applications or the prosecution history thereof and advise
the USPTO that the claims in this application may be broader than
any claim in the parent applications.
COPYRIGHT NOTICE
[0002] A portion of the disclosure of this patent document contains
material which is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or the patent disclosure, as it appears in the
Patent and Trademark Office patent file or records, but otherwise
reserves all copyright or rights whatsoever. .COPYRGT. 2020 The
Climate Corporation.
FIELD OF THE DISCLOSURE
[0003] The technical field of the present disclosure includes
computer systems useful in agriculture and climatology. The
disclosure is also in the technical field of computer systems that
are programmed or configured to generate management zones for
agricultural fields based on digital historical yield map data,
pipelined data processing, and computer-implemented data
recommendations for use in agriculture.
BACKGROUND
[0004] The approaches described in this section are approaches that
could be pursued, but not necessarily approaches that have been
previously conceived or pursued. Therefore, unless otherwise
indicated, it should not be assumed that any of the approaches
described in this section qualify as prior art merely by virtue of
their inclusion in this section.
[0005] Management zones refer to contiguous regions within an
agricultural field that have similar limiting factors influencing
harvested yields of crops. The regions that belong to the same
management zone can usually be managed uniformly with respect to
seeding, irrigation, application of fertilizers such as nitrogen,
and/or harvesting.
[0006] One advantage of identifying management zones within an
agricultural field is that information about the zones may help
crop growers to customize their practices for each zone to thus
increase the productivity and the harvested yields of crops.
SUMMARY
[0007] The appended claims may serve as a summary of the
disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] In the drawings:
[0009] FIG. 1 illustrates an example computer system that is
configured to perform the functions described herein, shown in a
field environment with other apparatus with which the system may
interoperate.
[0010] FIG. 2 illustrates two views of an example logical
organization of sets of instructions in main memory when an example
mobile application is loaded for execution.
[0011] FIG. 3 illustrates a programmed process by which the
agricultural intelligence computer system generates one or more
preconfigured agronomic models using agronomic data provided by one
or more data sources.
[0012] FIG. 4 is a block diagram that illustrates a computer system
400 upon which an embodiment of the invention may be
implemented.
[0013] FIG. 5 depicts an example embodiment of a timeline view for
data entry.
[0014] FIG. 6 depicts an example embodiment of a spreadsheet view
for data entry.
[0015] FIG. 7 depicts an example embodiment of a management zone
creation pipeline.
[0016] FIG. 8 depicts a method for creating management zones for an
agricultural field.
[0017] FIG. 9 depicts a method for management zones
post-processing.
DETAILED DESCRIPTION
[0018] In the following description, for the purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the present disclosure. It will
be apparent, however, that embodiments may be practiced without
these specific details. In other instances, well-known structures
and devices are shown in block diagram form in order to avoid
unnecessarily obscuring the present disclosure. Embodiments are
disclosed in sections according to the following outline: [0019] 1.
GENERAL OVERVIEW [0020] 2. EXAMPLE AGRICULTURAL INTELLIGENCE
COMPUTER SYSTEM [0021] 2.1. STRUCTURAL OVERVIEW [0022] 2.2.
APPLICATION PROGRAM OVERVIEW [0023] 2.3. DATA INGEST TO THE
COMPUTER SYSTEM [0024] 2.4. PROCESS OVERVIEW--AGRONOMIC MODEL
TRAINING [0025] 2.5. IMPLEMENTATION EXAMPLE--HARDWARE OVERVIEW
[0026] 3. IDENTIFYING MANAGEMENT ZONES BASED ON HISTORICAL YIELD
DATA [0027] 3.1 MANAGEMENT ZONES CREATING PIPELINE [0028] 3.2
CREATING MANAGEMENT ZONES [0029] 3.2.1 YIELD DATA [0030] 3.2.2
PREPROCESSING OF RECEIVED DATA [0031] 3.2.3 IMPUTING MISSING YIELD
DATA [0032] 3.2.4 EMPIRICAL CUMULATIVE DENSITY TRANSFORMATION
[0033] 3.2.5 SPATIAL SMOOTHING [0034] 3.2.6 IDENTIFYING MANAGEMENT
ZONES [0035] 3.2.6.1 K-MEANS APPROACH [0036] 3.2.6.2 FUZZY C-MEANS
APPROACH [0037] 3.2.6.3 REGION MERGING APPROACH [0038] 3.2.7 POST
PROCESSING [0039] 3.3 PERFORMANCE CONSIDERATIONS [0040] 3.4
USEFULNESS OF MANAGEMENT ZONES DELINEATION [0041] 3.5
EXTENSIONS
[0042] 1. General Overview
[0043] In an embodiment, a process of delineating management zones
within an agricultural field management zones includes determining
contiguous subregions within the agricultural field that have
similar yield limiting factors, and thus can be uniformly
managed.
[0044] In an embodiment, a process of delineating management zones
within an agricultural field is implemented in a computer system
that comprises computer memory and one or more processors
configured to execute program instructions. The process may be
implemented for example, in a computer workstation owned by a crop
grower and to which the grower may provide historical yields of
crops data. The process may also be implemented in a mobile device,
such as a smart phone that connects to a storage device or a cloud
storage system in which the historical yields of crops data is
stored. Furthermore, the process may be implemented in a computer
server to which the yield data is provided or to which the yield
data is made available.
[0045] A zone delineation process may include using instructions
programmed in a computer system to receive yield data representing
yields of crops that have been harvested from an agricultural
field. The yield data may include historical maps provided by crops
growers, research partners, agricultural agencies, and other
agricultural data sources. The yield data may comprise data
representing yield information collected for one year or multiple
years.
[0046] In an embodiment, received yield data is preprocessed.
Preprocessing of the yield data may be performed by removing yield
maps that correspond to multiple crops planted in the same season
in the agricultural field, removing yield maps that include yield
records outside boundaries of the agricultural field, marking yield
records of absolute zeros as missing values, removing yield records
for fields that have less than two years of yield maps period, or
removing yield maps that have more than a certain count of missing
values.
[0047] A zone delineation process may also include using the
instructions to transform yield data to generate transformed yield
data. Transforming the yield data may be performed by applying an
empirical cumulative density function (ECDF) to the yield data to
normalize the data to a certain range, such as a range of [0, 1].
The transformed yield data may be comparable across different years
and types of crops. For example, the ECDF may allow transforming,
or normalizing, yield records for each field and year, regardless
of the crop type and the collection time, to a range of [0, 1], so
that the transformed data may be comparable with each other.
[0048] In an embodiment, transformed yield data is used to generate
smooth transformed yield data. Smooth transformed yield data may be
generated by performing smoothing operations on the transformed
yield data. That may include removing outlier data from the
transformed yield data, determining missing values in the
transformed yield data, computing substitute values for the missing
values, or performing a spatial smoothing on the transformed yield
data.
[0049] In an embodiment, a process of delineating management zones
within an agricultural field includes determining a first count
value for a plurality of management classes. A count of management
classes is a parameter used to determine a count of distinctive
management classes to be used to create the management zones.
Distinctive management classes include the areas in the
agricultural field that have relatively homogeneous yield limiting
factors, but are not restricted to be spatially contiguous.
[0050] The smooth transformed yield data and the first count value
may be used to generate a plurality of first management zones. The
first management zones may be generated by applying, to the smooth
transformed yield data, clustering approaches, such as a
centroid-based clustering, or region merging approaches.
[0051] If a plurality of first management zones includes one or
more relatively small zones, then the small zones may be merged
with their neighboring larger zones. Merging the small management
zones with their respective neighboring large zones results in
generating a set of first merged management zones.
[0052] In an embodiment, a set of first merged management zones is
used to automatically control a computer control system of one or
more of seeding, irrigation, nitrogen application, and harvesting
apparatus.
[0053] In an embodiment, a set of first merged management zones and
a first count value are stored in a set of management metrics. The
metrics may be stored in for example, a computer memory unit, a
data storage, or a cloud storage system.
[0054] The process of creating management zones may be repeated for
different counts of classes. For example, the process may be
repeated for the increased, or decreased, counts until a desired
quality of the zone delineation is achieved.
[0055] In an embodiment, a second count value for a plurality of
management classes is determined and used to generate a plurality
of second management zones. The plurality of second management
zones may be generated by applying the same approaches as those
used to generate a plurality of first management zones. The
approaches may include clustering approaches, such as a
centroid-based clustering, and region merging approaches.
[0056] If a plurality of second management zones includes one or
more relatively small zones, then the small zones may be merged
with their corresponding neighboring larger zones to generate a set
of second merged management zones. The set of second merged
management zones and the second count value may be stored as
metrics of management zones.
[0057] In an embodiment, recommendations are generated based on the
metrics of management zones. The recommendations may be generated
by evaluating a delineation quality of management zones stored in
the metrics. The recommendations may include information about the
created management zones and a recommended class count for the
plurality of management classes. The recommendations may be sent to
crop growers to help them to determine for example, seeding
schedules for the field.
[0058] 2. Example Agricultural Intelligence Computer System
[0059] 2.1 Structural Overview
[0060] FIG. 1 illustrates an example computer system that is
configured to perform the functions described herein, shown in a
field environment with other apparatus with which the system may
interoperate. In one embodiment, a user 102 owns, operates or
possesses a field manager computing device 104 in a field location
or associated with a field location such as a field intended for
agricultural activities or a management location for one or more
agricultural fields. The field manager computer device 104 is
programmed or configured to provide field data 106 to an
agricultural intelligence computer system 130 via one or more
networks 109.
[0061] Examples of field data 106 include (a) identification data
(for example, acreage, field name, 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), (b) harvest data (for example, 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, and previous
growing season information), (c) soil data (for example, type,
composition, pH, organic matter (OM), cation exchange capacity
(CEC)), (d) planting data (for example, planting date, seed(s)
type, relative maturity (RM) of planted seed(s), seed population),
(e) fertilizer data (for example, nutrient type (Nitrogen,
Phosphorous, Potassium), application type, application date,
amount, source, method), (f) pesticide data (for example,
pesticide, herbicide, fungicide, other substance or mixture of
substances intended for use as a plant regulator, defoliant, or
desiccant, application date, amount, source, method), (g)
irrigation data (for example, application date, amount, source,
method), (h) weather data (for example, precipitation, rainfall
rate, predicted rainfall, water runoff rate region, temperature,
wind, forecast, pressure, visibility, clouds, heat index, dew
point, humidity, snow depth, air quality, sunrise, sunset), (i)
imagery data (for example, imagery and light spectrum information
from an agricultural apparatus sensor, camera, computer,
smartphone, tablet, unmanned aerial vehicle, planes or satellite),
(j) 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)),
and (k) soil, seed, crop phenology, pest and disease reporting, and
predictions sources and databases.
[0062] A data server computer 108 is communicatively coupled to
agricultural intelligence computer system 130 and is programmed or
configured to send external data 110 to agricultural intelligence
computer system 130 via the network(s) 109. The external data
server computer 108 may be owned or operated by the same legal
person or entity as the agricultural intelligence computer system
130, or by a different person or entity such as a government
agency, non-governmental organization (NGO), and/or a private data
service provider. Examples of external data include weather data,
imagery data, soil data, or statistical data relating to crop
yields, among others. External data 110 may consist of the same
type of information as field data 106. In some embodiments, the
external data 110 is provided by an external data server 108 owned
by the same entity that owns and/or operates the agricultural
intelligence computer system 130. For example, the agricultural
intelligence computer system 130 may include a data server focused
exclusively on a type of data that might otherwise be obtained from
third party sources, such as weather data. In some embodiments, an
external data server 108 may actually be incorporated within the
system 130.
[0063] An agricultural apparatus 111 may have one or more remote
sensors 112 fixed thereon, which sensors are communicatively
coupled either directly or indirectly via agricultural apparatus
111 to the agricultural intelligence computer system 130 and are
programmed or configured to send sensor data to agricultural
intelligence computer system 130. Examples of agricultural
apparatus 111 include tractors, combines, harvesters, planters,
trucks, fertilizer equipment, unmanned aerial vehicles, and any
other item of physical machinery or hardware, typically mobile
machinery, and which may be used in tasks associated with
agriculture. In some embodiments, a single unit of apparatus 111
may comprise a plurality of sensors 112 that are coupled locally in
a network on the apparatus; controller area network (CAN) is
example of such a network that can be installed in combines or
harvesters. Application controller 114 is communicatively coupled
to agricultural intelligence computer system 130 via the network(s)
109 and is programmed or configured to receive one or more scripts
to control an operating parameter of an agricultural vehicle or
implement from the agricultural intelligence computer system 130.
For instance, a controller area network (CAN) bus interface may be
used to enable communications from the agricultural intelligence
computer system 130 to the agricultural apparatus 111, such as how
the CLIMATE FIELDVIEW DRIVE, available from The Climate
Corporation, San Francisco, Calif., is used. Sensor data may
consist of the same type of information as field data 106. In some
embodiments, remote sensors 112 may not be fixed to an agricultural
apparatus 111 but may be remotely located in the field and may
communicate with network 109.
[0064] The apparatus 111 may comprise a cab computer 115 that is
programmed with a cab application, which may comprise a version or
variant of the mobile application for device 104 that is further
described in other sections herein. In an embodiment, cab computer
115 comprises a compact computer, often a tablet-sized computer or
smartphone, with a graphical screen display, such as a color
display, that is mounted within an operator's cab of the apparatus
111. Cab computer 115 may implement some or all of the operations
and functions that are described further herein for the mobile
computer device 104.
[0065] The network(s) 109 broadly represent any combination of one
or more data communication networks including local area networks,
wide area networks, internetworks or internets, using any of
wireline or wireless links, including terrestrial or satellite
links. The network(s) may be implemented by any medium or mechanism
that provides for the exchange of data between the various elements
of FIG. 1. The various elements of FIG. 1 may also have direct
(wired or wireless) communications links. The sensors 112,
controller 114, external data server computer 108, and other
elements of the system each comprise an interface compatible with
the network(s) 109 and are programmed or configured to use
standardized protocols for communication across the networks such
as TCP/IP, Bluetooth, CAN protocol and higher-layer protocols such
as HTTP, TLS, and the like.
[0066] Agricultural intelligence computer system 130 is programmed
or configured to receive field data 106 from field manager
computing device 104, external data 110 from external data server
computer 108, and sensor data from remote sensor 112. Agricultural
intelligence computer system 130 may be further configured to host,
use or execute one or more computer programs, other software
elements, digitally programmed logic such as FPGAs or ASICs, or any
combination thereof to perform translation and storage of data
values, construction of digital models of one or more crops on one
or more fields, generation of recommendations and notifications,
and generation and sending of scripts to application controller
114, in the manner described further in other sections of this
disclosure.
[0067] In an embodiment, agricultural intelligence computer system
130 is programmed with or comprises a communication layer 132,
presentation layer 134, data management layer 140,
hardware/virtualization layer 150, and model and field data
repository 160. "Layer," in this context, refers to any combination
of electronic digital interface circuits, microcontrollers,
firmware such as drivers, and/or computer programs or other
software elements.
[0068] Communication layer 132 may be programmed or configured to
perform input/output interfacing functions including sending
requests to field manager computing device 104, external data
server computer 108, and remote sensor 112 for field data, external
data, and sensor data respectively. Communication layer 132 may be
programmed or configured to send the received data to model and
field data repository 160 to be stored as field data 106.
[0069] In an embodiment, agricultural intelligence computer system
130 is programmed with or comprises code instructions 180. For
example, code instructions 180 may include data receiving
instructions 182 which are programmed for receiving, over
network(s) 109, electronic digital data comprising yield data. Code
instructions 180 may also include data processing instructions 183
which are programmed for preprocessing of the received yield data;
data smoothing instructions 184 which are programmed for smoothing
the preprocessed yield data; data delineating instructions 187
which are programmed for delineating management zones;
post-processing instructions 186 which are programmed for
post-processing of the delineated management zones; data comparison
instructions 185 which are programmed for comparing the
post-processed management zones; and other detection instructions
188.
[0070] Presentation layer 134 may be programmed or configured to
generate a graphical user interface (GUI) to be displayed on field
manager computing device 104, cab computer 115 or other computers
that are coupled to the system 130 through the network 109. The GUI
may comprise controls for inputting data to be sent to agricultural
intelligence computer system 130, generating requests for models
and/or recommendations, and/or displaying recommendations,
notifications, models, and other field data.
[0071] Data management layer 140 may be programmed or configured to
manage read operations and write operations involving the
repository 160 and other functional elements of the system,
including queries and result sets communicated between the
functional elements of the system and the repository. Examples of
data management layer 140 include JDBC, SQL server interface code,
and/or HADOOP interface code, among others. Repository 160 may
comprise a database. 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 comprise 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. Examples of RDBMS's
include, but are not limited to including, ORACLE.RTM., MYSQL,
IBM.RTM. DB2, MICROSOFT.RTM. SQL SERVER, SYBASE.RTM., and
POSTGRESQL databases. However, any database may be used that
enables the systems and methods described herein.
[0072] When field data 106 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
specify identification 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 102 may specify
identification data by accessing a map on the user device (served
by the agricultural intelligence computer system 130) and drawing
boundaries of the field over the map. Such CLU selection or map
drawings represent geographic identifiers. In alternative
embodiments, the user may specify identification data by accessing
field identification 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
identification data to the agricultural intelligence computer
system.
[0073] In an example embodiment, the agricultural intelligence
computer system 130 is programmed to generate and cause displaying
a graphical user interface comprising a data manager for data
input. After one or more fields have been identified using the
methods described above, the data manager may provide one or more
graphical user interface widgets which when selected can identify
changes to the field, soil, crops, tillage, or nutrient practices.
The data manager may include a timeline view, a spreadsheet view,
and/or one or more editable programs.
[0074] FIG. 5 depicts an example embodiment of a timeline view for
data entry. Using the display depicted in FIG. 5, a user computer
can input a selection of a particular field and a particular date
for the addition of event. Events depicted at the top of the
timeline may include Nitrogen, Planting, Practices, and Soil. To
add a nitrogen application event, a user computer may provide input
to select the nitrogen tab. The user computer may then select a
location on the timeline for a particular field in order to
indicate an application of nitrogen on the selected field. In
response to receiving a selection of a location on the timeline for
a particular field, the data manager may display a data entry
overlay, allowing the user computer to input data pertaining to
nitrogen applications, planting procedures, soil application,
tillage procedures, irrigation practices, or other information
relating to the particular field. For example, if a user computer
selects a portion of the timeline and indicates an application of
nitrogen, then the data entry overlay may include fields for
inputting an amount of nitrogen applied, a date of application, a
type of fertilizer used, and any other information related to the
application of nitrogen.
[0075] In an embodiment, the data manager provides an interface for
creating one or more programs. "Program," in this context, refers
to a set of data pertaining to nitrogen applications, planting
procedures, soil application, tillage procedures, irrigation
practices, or other information that may be related to one or more
fields, and that can be stored in digital data storage for reuse as
a set in other operations. After a program has been created, it may
be conceptually applied to one or more fields and references to the
program may be stored in digital storage in association with data
identifying the fields. Thus, instead of manually entering
identical data relating to the same nitrogen applications for
multiple different fields, a user computer may create a program
that indicates a particular application of nitrogen and then apply
the program to multiple different fields. For example, in the
timeline view of FIG. 5, the top two timelines have the "Fall
applied" program selected, which includes an application of 150 lbs
N/ac in early April. The data manager may provide an interface for
editing a program. In an embodiment, when a particular program is
edited, each field that has selected the particular program is
edited. For example, in FIG. 5, if the "Fall applied" program is
edited to reduce the application of nitrogen to 130 lbs N/ac, the
top two fields may be updated with a reduced application of
nitrogen based on the edited program.
[0076] In an embodiment, in response to receiving edits to a field
that has a program selected, the data manager removes the
correspondence of the field to the selected program. For example,
if a nitrogen application is added to the top field in FIG. 5, the
interface may update to indicate that the "Fall applied" program is
no longer being applied to the top field. While the nitrogen
application in early April may remain, updates to the "Fall
applied" program would not alter the April application of
nitrogen.
[0077] FIG. 6 depicts an example embodiment of a spreadsheet view
for data entry. Using the display depicted in FIG. 6, a user can
create and edit information for one or more fields. The data
manager may include spreadsheets for inputting information with
respect to Nitrogen, Planting, Practices, and Soil as depicted in
FIG. 6. To edit a particular entry, a user computer may select the
particular entry in the spreadsheet and update the values. For
example, FIG. 6 depicts an in-progress update to a target yield
value for the second field. Additionally, a user computer may
select one or more fields in order to apply one or more programs.
In response to receiving a selection of a program for a particular
field, the data manager may automatically complete the entries for
the particular field based on the selected program. As with the
timeline view, the data manager may update the entries for each
field associated with a particular program in response to receiving
an update to the program. Additionally, the data manager may remove
the correspondence of the selected program to the field in response
to receiving an edit to one of the entries for the field.
[0078] In an embodiment, model and field data is stored in model
and field data repository 160. Model data comprises data models
created for one or more fields. For example, a crop model may
include a digitally constructed model of the development of a crop
on the one or more fields. "Model," in this context, refers to an
electronic digitally stored set of executable instructions and data
values, associated with one another, which are capable of receiving
and responding to a programmatic or other digital call, invocation,
or request for resolution based upon specified input values, to
yield one or more stored output values that can serve as the basis
of computer-implemented recommendations, output data displays, or
machine control, among other things. Persons of skill in the field
find it convenient to express models using mathematical equations,
but that form of expression does not confine the models disclosed
herein to abstract concepts; instead, each model herein has a
practical application in a computer in the form of stored
executable instructions and data that implement the model using the
computer. The model data may include a model of past events on the
one or more fields, a model of the current status of the one or
more fields, and/or a model of predicted events on the one or more
fields. Model and field data may be stored in data structures in
memory, rows in a database table, in flat files or spreadsheets, or
other forms of stored digital data.
[0079] Hardware/virtualization layer 150 comprises one or more
central processing units (CPUs), memory controllers, and other
devices, components, or elements of a computer system such as
volatile or non-volatile memory, non-volatile storage such as disk,
and I/O devices or interfaces as illustrated and described, for
example, in connection with FIG. 4. The layer 150 also may comprise
programmed instructions that are configured to support
virtualization, containerization, or other technologies.
[0080] For purposes of illustrating a clear example, FIG. 1 shows a
limited number of instances of certain functional elements.
However, in other embodiments, there may be any number of such
elements. For example, embodiments may use thousands or millions of
different mobile computing devices 104 associated with different
users. Further, the system 130 and/or external data server computer
108 may be implemented using two or more processors, cores,
clusters, or instances of physical machines or virtual machines,
configured in a discrete location or co-located with other elements
in a datacenter, shared computing facility or cloud computing
facility.
[0081] 2.2. Application Program Overview
[0082] In an embodiment, the implementation of the functions
described herein using one or more computer programs or other
software elements that are loaded into and executed using one or
more general-purpose computers will cause the general-purpose
computers to be configured as a particular machine or as a computer
that is specially adapted to perform the functions described
herein. Further, each of the flow diagrams that are described
further herein may serve, alone or in combination with the
descriptions of processes and functions in prose herein, as
algorithms, plans or directions that may be used to program a
computer or logic to implement the functions that are described. In
other words, all the prose text herein, and all the drawing
figures, together are intended to provide disclosure of algorithms,
plans or directions that are sufficient to permit a skilled person
to program a computer to perform the functions that are described
herein, in combination with the skill and knowledge of such a
person given the level of skill that is appropriate for inventions
and disclosures of this type.
[0083] In an embodiment, user 102 interacts with agricultural
intelligence computer system 130 using field manager computing
device 104 configured with an operating system and one or more
application programs or apps; the field manager computing device
104 also may interoperate with the agricultural intelligence
computer system independently and automatically under program
control or logical control and direct user interaction is not
always required. Field manager computing device 104 broadly
represents one or more of a smart phone, PDA, tablet computing
device, laptop computer, desktop computer, workstation, or any
other computing device capable of transmitting and receiving
information and performing the functions described herein. Field
manager computing device 104 may communicate via a network using a
mobile application stored on field manager computing device 104,
and in some embodiments, the device may be coupled using a cable
113 or connector to the sensor 112 and/or controller 114. A
particular user 102 may own, operate or possess and use, in
connection with system 130, more than one field manager computing
device 104 at a time.
[0084] The mobile application may provide client-side
functionality, via the network to one or more mobile computing
devices. In an example embodiment, field manager computing device
104 may access the mobile application via a web browser or a local
client application or app. Field manager computing device 104 may
transmit data to, and receive data from, one or more front-end
servers, using web-based protocols or formats such as HTTP, XML
and/or JSON, or app-specific protocols. In an example embodiment,
the data may take the form of requests and user information input,
such as field data, into the mobile computing device. In some
embodiments, the mobile application interacts with location
tracking hardware and software on field manager computing device
104 which determines the location of field manager computing device
104 using standard tracking techniques such as multilateration of
radio signals, the global positioning system (GPS), WiFi
positioning systems, or other methods of mobile positioning. In
some cases, location data or other data associated with the device
104, user 102, and/or user account(s) may be obtained by queries to
an operating system of the device or by requesting an app on the
device to obtain data from the operating system.
[0085] In an embodiment, field manager computing device 104 sends
field data 106 to agricultural intelligence computer system 130
comprising or including, but not limited to, data values
representing one or more of: a geographical location of the one or
more fields, tillage information for the one or more fields, crops
planted in the one or more fields, and soil data extracted from the
one or more fields. Field manager computing device 104 may send
field data 106 in response to user input from user 102 specifying
the data values for the one or more fields. Additionally, field
manager computing device 104 may automatically send field data 106
when one or more of the data values becomes available to field
manager computing device 104. For example, field manager computing
device 104 may be communicatively coupled to remote sensor 112
and/or application controller 114. In response to receiving data
indicating that application controller 114 released water onto the
one or more fields, field manager computing device 104 may send
field data 106 to agricultural intelligence computer system 130
indicating that water was released on the one or more fields. Field
data 106 identified in this disclosure may be input and
communicated using electronic digital data that is communicated
between computing devices using parameterized URLs over HTTP, or
another suitable communication or messaging protocol.
[0086] A commercial example of the mobile application is CLIMATE
FIELDVIEW, commercially available from The Climate Corporation, San
Francisco, Calif. The CLIMATE FIELDVIEW application, or other
applications, may be modified, extended, or adapted to include
features, functions, and programming that have not been disclosed
earlier than the filing date of this disclosure. In one embodiment,
the mobile application comprises an integrated software platform
that allows a grower to make fact-based decisions for their
operation because it combines historical data about the grower's
fields with any other data that the grower wishes to compare. The
combinations and comparisons may be performed in real time and are
based upon scientific models that provide potential scenarios to
permit the grower to make better, more informed decisions.
[0087] FIG. 2 illustrates two views of an example logical
organization of sets of instructions in main memory when an example
mobile application is loaded for execution. In FIG. 2, each named
element represents a region of one or more pages of RAM or other
main memory, or one or more blocks of disk storage or other
non-volatile storage, and the programmed instructions within those
regions. In one embodiment, in view (a), a mobile computer
application 200 comprises account-fields-data ingestion-sharing
instructions 202, overview and alert instructions 204, digital map
book instructions 206, seeds and planting instructions 208,
nitrogen instructions 210, weather instructions 212, field health
instructions 214, and performance instructions 216.
[0088] In one embodiment, a mobile computer application 200
comprises account-fields-data ingestion-sharing instructions 202
which are programmed to receive, translate, and ingest field data
from third party systems via manual upload or APIs. Data types may
include field boundaries, yield maps, as-planted maps, soil test
results, as-applied maps, and/or management zones, among others.
Data formats may include shape files, native data formats of third
parties, and/or farm management information system (FMIS) exports,
among others. Receiving data may occur via manual upload, e-mail
with attachment, external APIs that push data to the mobile
application, or instructions that call APIs of external systems to
pull data into the mobile application. In one embodiment, mobile
computer application 200 comprises a data inbox. In response to
receiving a selection of the data inbox, the mobile computer
application 200 may display a graphical user interface for manually
uploading data files and importing uploaded files to a data
manager.
[0089] In one embodiment, digital map book instructions 206
comprise field map data layers stored in device memory and are
programmed with data visualization tools and geospatial field
notes. This provides growers with convenient information close at
hand for reference, logging and visual insights into field
performance. In one embodiment, overview and alert instructions 204
are programmed to provide an operation-wide view of what is
important to the grower, and timely recommendations to take action
or focus on particular issues. This permits the grower to focus
time on what needs attention, to save time and preserve yield
throughout the season. In one embodiment, seeds and planting
instructions 208 are programmed to provide tools for seed
selection, hybrid placement, and script creation, including
variable rate (VR) script creation, based upon scientific models
and empirical data. This enables growers to maximize yield or
return on investment through optimized seed purchase, placement and
population.
[0090] In one embodiment, script generation instructions 205 are
programmed to provide an interface for generating scripts,
including variable rate (VR) fertility scripts. The interface
enables growers to create scripts for field implements, such as
nutrient applications, planting, and irrigation. For example, a
planting script interface may comprise tools for identifying a type
of seed for planting. Upon receiving a selection of the seed type,
mobile computer application 200 may display one or more fields
broken into management zones, such as the field map data layers
created as part of digital map book instructions 206. In one
embodiment, the management zones comprise soil zones along with a
panel identifying each soil zone and a soil name, texture, drainage
for each zone, or other field data. Mobile computer application 200
may also display tools for editing or creating such, such as
graphical tools for drawing management zones, such as soil zones,
over a map of one or more fields. Planting procedures may be
applied to all management zones or different planting procedures
may be applied to different subsets of management zones. When a
script is created, mobile computer application 200 may make the
script available for download in a format readable by an
application controller, such as an archived or compressed format.
Additionally and/or alternatively, a script may be sent directly to
cab computer 115 from mobile computer application 200 and/or
uploaded to one or more data servers and stored for further use. In
one embodiment, nitrogen instructions 210 are programmed to provide
tools to inform nitrogen decisions by visualizing the availability
of nitrogen to crops. This enables growers to maximize yield or
return on investment through optimized nitrogen application during
the season. Example programmed functions include displaying images
such as SSURGO images to enable drawing of application zones and/or
images generated from subfield soil data, such as data obtained
from sensors, at a high spatial resolution (as fine as 10 meters or
smaller because of their proximity to the soil); upload of existing
grower-defined zones; providing an application graph and/or a map
to enable tuning application(s) of nitrogen across multiple zones;
output of scripts to drive machinery; tools for mass data entry and
adjustment; and/or maps for data visualization, among others. "Mass
data entry," in this context, may mean entering data once and then
applying the same data to multiple fields that have been defined in
the system; example data may include nitrogen application data that
is the same for many fields of the same grower, but such mass data
entry applies to the entry of any type of field data into the
mobile computer application 200. For example, nitrogen instructions
210 may be programmed to accept definitions of nitrogen planting
and practices programs and to accept user input specifying to apply
those programs across multiple fields. "Nitrogen planting
programs," in this context, refers to a stored, named set of data
that associates: a name, color code or other identifier, one or
more dates of application, types of material or product for each of
the dates and amounts, method of application or incorporation such
as injected or knifed in, and/or amounts or rates of application
for each of the dates, crop or hybrid that is the subject of the
application, among others. "Nitrogen practices programs," in this
context, refers to a stored, named set of data that associates: a
practices name; a previous crop; a tillage system; a date of
primarily tillage; one or more previous tillage systems that were
used; one or more indicators of application type, such as manure,
that were used. Nitrogen instructions 210 also may be programmed to
generate and cause displaying a nitrogen graph, which indicates
projections of plant use of the specified nitrogen and whether a
surplus or shortfall is predicted; in some embodiments, different
color indicators may signal a magnitude of surplus or magnitude of
shortfall. In one embodiment, a nitrogen graph comprises a
graphical display in a computer display device comprising a
plurality of rows, each row associated with and identifying a
field; data specifying what crop is planted in the field, the field
size, the field location, and a graphic representation of the field
perimeter; in each row, a timeline by month with graphic indicators
specifying each nitrogen application and amount at points
correlated to month names; and numeric and/or colored indicators of
surplus or shortfall, in which color indicates magnitude.
[0091] In one embodiment, the nitrogen graph may include one or
more user input features, such as dials or slider bars, to
dynamically change the nitrogen planting and practices programs so
that a user may optimize his nitrogen graph. The user may then use
his optimized nitrogen graph and the related nitrogen planting and
practices programs to implement one or more scripts, including
variable rate (VR) fertility scripts. Nitrogen instructions 210
also may be programmed to generate and cause displaying a nitrogen
map, which indicates projections of plant use of the specified
nitrogen and whether a surplus or shortfall is predicted; in some
embodiments, different color indicators may signal a magnitude of
surplus or magnitude of shortfall. The nitrogen map may display
projections of plant use of the specified nitrogen and whether a
surplus or shortfall is predicted for different times in the past
and the future (such as daily, weekly, monthly or yearly) using
numeric and/or colored indicators of surplus or shortfall, in which
color indicates magnitude. In one embodiment, the nitrogen map may
include one or more user input features, such as dials or slider
bars, to dynamically change the nitrogen planting and practices
programs so that a user may optimize his nitrogen map, such as to
obtain a preferred amount of surplus to shortfall. The user may
then use his optimized nitrogen map and the related nitrogen
planting and practices programs to implement one or more scripts,
including variable rate (VR) fertility scripts. In other
embodiments, similar instructions to the nitrogen instructions 210
could be used for application of other nutrients (such as
phosphorus and potassium) application of pesticide, and irrigation
programs.
[0092] In one embodiment, weather instructions 212 are programmed
to provide field-specific recent weather data and forecasted
weather information. This enables growers to save time and have an
efficient integrated display with respect to daily operational
decisions.
[0093] In one embodiment, field health instructions 214 are
programmed to provide timely remote sensing images highlighting
in-season crop variation and potential concerns. Example programmed
functions include cloud checking, to identify possible clouds or
cloud shadows; determining nitrogen indices based on field images;
graphical visualization of scouting layers, including, for example,
those related to field health, and viewing and/or sharing of
scouting notes; and/or downloading satellite images from multiple
sources and prioritizing the images for the grower, among
others.
[0094] In one embodiment, performance instructions 216 are
programmed to provide reports, analysis, and insight tools using
on-farm data for evaluation, insights and decisions. This enables
the grower to seek improved outcomes for the next year through
fact-based conclusions about why return on investment was at prior
levels, and insight into yield-limiting factors. The performance
instructions 216 may be programmed to communicate via the
network(s) 109 to back-end analytics programs executed at
agricultural intelligence computer system 130 and/or external data
server computer 108 and configured to analyze metrics such as
yield, hybrid, population, SSURGO, soil tests, or elevation, among
others. Programmed reports and analysis may include yield
variability analysis, benchmarking of yield and other metrics
against other growers based on anonymized data collected from many
growers, or data for seeds and planting, among others.
[0095] Applications having instructions configured in this way may
be implemented for different computing device platforms while
retaining the same general user interface appearance. For example,
the mobile application may be programmed for execution on tablets,
smartphones, or server computers that are accessed using browsers
at client computers. Further, the mobile application as configured
for tablet computers or smartphones may provide a full app
experience or a cab app experience that is suitable for the display
and processing capabilities of cab computer 115. For example,
referring now to view (b) of FIG. 2, in one embodiment a cab
computer application 220 may comprise maps-cab instructions 222,
remote view instructions 224, data collect and transfer
instructions 226, machine alerts instructions 228, script transfer
instructions 230, and scouting-cab instructions 232. The code base
for the instructions of view (b) may be the same as for view (a)
and executables implementing the code may be programmed to detect
the type of platform on which they are executing and to expose,
through a graphical user interface, only those functions that are
appropriate to a cab platform or full platform. This approach
enables the system to recognize the distinctly different user
experience that is appropriate for an in-cab environment and the
different technology environment of the cab. The maps-cab
instructions 222 may be programmed to provide map views of fields,
farms or regions that are useful in directing machine operation.
The remote view instructions 224 may be programmed to turn on,
manage, and provide views of machine activity in real-time or near
real-time to other computing devices connected to the system 130
via wireless networks, wired connectors or adapters, and the like.
The data collect and transfer instructions 226 may be programmed to
turn on, manage, and provide transfer of data collected at machine
sensors and controllers to the system 130 via wireless networks,
wired connectors or adapters, and the like. The machine alerts
instructions 228 may be programmed to detect issues with operations
of the machine or tools that are associated with the cab and
generate operator alerts. The script transfer instructions 230 may
be configured to transfer in scripts of instructions that are
configured to direct machine operations or the collection of data.
The scouting-cab instructions 230 may be programmed to display
location-based alerts and information received from the system 130
based on the location of the agricultural apparatus 111 or sensors
112 in the field and ingest, manage, and provide transfer of
location-based scouting observations to the system 130 based on the
location of the agricultural apparatus 111 or sensors 112 in the
field.
[0096] 2.3. Data Ingest to the Computer System
[0097] In an embodiment, external data server computer 108 stores
external data 110, including soil data representing soil
composition for the one or more fields and weather data
representing temperature and precipitation on the one or more
fields. The weather data may include past and present weather data
as well as forecasts for future weather data. In an embodiment,
external data server computer 108 comprises a plurality of servers
hosted by different entities. For example, a first server may
contain soil composition data while a second server may include
weather data. Additionally, soil composition data may be stored in
multiple servers. For example, one server may store data
representing percentage of sand, silt, and clay in the soil while a
second server may store data representing percentage of organic
matter (OM) in the soil.
[0098] In an embodiment, remote sensor 112 comprises one or more
sensors that are programmed or configured to produce one or more
observations. Remote sensor 112 may be aerial sensors, such as
satellites, vehicle sensors, planting equipment sensors, tillage
sensors, fertilizer or insecticide application sensors, harvester
sensors, and any other implement capable of receiving data from the
one or more fields. In an embodiment, application controller 114 is
programmed or configured to receive instructions from agricultural
intelligence computer system 130. Application controller 114 may
also be programmed or configured to control an operating parameter
of an agricultural vehicle or implement. For example, an
application controller may be programmed or configured to control
an operating parameter of a vehicle, such as a tractor, planting
equipment, tillage equipment, fertilizer or insecticide equipment,
harvester equipment, or other farm implements such as a water
valve. Other embodiments may use any combination of sensors and
controllers, of which the following are merely selected
examples.
[0099] The system 130 may obtain or ingest data under user 102
control, on a mass basis from a large number of growers who have
contributed data to a shared database system. This form of
obtaining data may be termed "manual data ingest" as one or more
user-controlled computer operations are requested or triggered to
obtain data for use by the system 130. As an example, the CLIMATE
FIELDVIEW application, commercially available from The Climate
Corporation, San Francisco, Calif., may be operated to export data
to system 130 for storing in the repository 160.
[0100] For example, seed monitor systems can both control planter
apparatus components and obtain planting data, including signals
from seed sensors via a signal harness that comprises a CAN
backbone and point-to-point connections for registration and/or
diagnostics. Seed monitor systems can be programmed or configured
to display seed spacing, population and other information to the
user via the cab computer 115 or other devices within the system
130. Examples are disclosed in U.S. Pat. No. 8,738,243 and US Pat.
Pub. 20150094916, and the present disclosure assumes knowledge of
those other patent disclosures.
[0101] Likewise, yield monitor systems may contain yield sensors
for harvester apparatus that send yield measurement data to the cab
computer 115 or other devices within the system 130. Yield monitor
systems may utilize one or more remote sensors 112 to obtain grain
moisture measurements in a combine or other harvester and transmit
these measurements to the user via the cab computer 115 or other
devices within the system 130.
[0102] In an embodiment, examples of sensors 112 that may be used
with any moving vehicle or apparatus of the type described
elsewhere herein include kinematic sensors and position sensors.
Kinematic sensors may comprise any of speed sensors such as radar
or wheel speed sensors, accelerometers, or gyros. Position sensors
may comprise GPS receivers or transceivers, or WiFi-based position
or mapping apps that are programmed to determine location based
upon nearby WiFi hotspots, among others.
[0103] In an embodiment, examples of sensors 112 that may be used
with tractors or other moving vehicles include engine speed
sensors, fuel consumption sensors, area counters or distance
counters that interact with GPS or radar signals, PTO (power
take-off) speed sensors, tractor hydraulics sensors configured to
detect hydraulics parameters such as pressure or flow, and/or and
hydraulic pump speed, wheel speed sensors or wheel slippage
sensors. In an embodiment, examples of controllers 114 that may be
used with tractors include hydraulic directional controllers,
pressure controllers, and/or flow controllers; hydraulic pump speed
controllers; speed controllers or governors; hitch position
controllers; or wheel position controllers provide automatic
steering.
[0104] In an embodiment, examples of sensors 112 that may be used
with seed planting equipment such as planters, drills, or air
seeders include seed sensors, which may be optical,
electromagnetic, or impact sensors; downforce sensors such as load
pins, load cells, pressure sensors; soil property sensors such as
reflectivity sensors, moisture sensors, electrical conductivity
sensors, optical residue sensors, or temperature sensors; component
operating criteria sensors such as planting depth sensors,
downforce cylinder pressure sensors, seed disc speed sensors, seed
drive motor encoders, seed conveyor system speed sensors, or vacuum
level sensors; or pesticide application sensors such as optical or
other electromagnetic sensors, or impact sensors. In an embodiment,
examples of controllers 114 that may be used with such seed
planting equipment include: toolbar fold controllers, such as
controllers for valves associated with hydraulic cylinders;
downforce controllers, such as controllers for valves associated
with pneumatic cylinders, airbags, or hydraulic cylinders, and
programmed for applying downforce to individual row units or an
entire planter frame; planting depth controllers, such as linear
actuators; metering controllers, such as electric seed meter drive
motors, hydraulic seed meter drive motors, or swath control
clutches; hybrid selection controllers, such as seed meter drive
motors, or other actuators programmed for selectively allowing or
preventing seed or an air-seed mixture from delivering seed to or
from seed meters or central bulk hoppers; metering controllers,
such as electric seed meter drive motors, or hydraulic seed meter
drive motors; seed conveyor system controllers, such as controllers
for a belt seed delivery conveyor motor; marker controllers, such
as a controller for a pneumatic or hydraulic actuator; or pesticide
application rate controllers, such as metering drive controllers,
orifice size or position controllers.
[0105] In an embodiment, examples of sensors 112 that may be used
with tillage equipment include position sensors for tools such as
shanks or discs; tool position sensors for such tools that are
configured to detect depth, gang angle, or lateral spacing;
downforce sensors; or draft force sensors. In an embodiment,
examples of controllers 114 that may be used with tillage equipment
include downforce controllers or tool position controllers, such as
controllers configured to control tool depth, gang angle, or
lateral spacing.
[0106] In an embodiment, examples of sensors 112 that may be used
in relation to apparatus for applying fertilizer, insecticide,
fungicide and the like, such as on-planter starter fertilizer
systems, subsoil fertilizer applicators, or fertilizer sprayers,
include: fluid system criteria sensors, such as flow sensors or
pressure sensors; sensors indicating which spray head valves or
fluid line valves are open; sensors associated with tanks, such as
fill level sensors; sectional or system-wide supply line sensors,
or row-specific supply line sensors; or kinematic sensors such as
accelerometers disposed on sprayer booms. In an embodiment,
examples of controllers 114 that may be used with such apparatus
include pump speed controllers; valve controllers that are
programmed to control pressure, flow, direction, PWM and the like;
or position actuators, such as for boom height, subsoiler depth, or
boom position.
[0107] In an embodiment, examples of sensors 112 that may be used
with harvesters include yield monitors, such as impact plate strain
gauges or position sensors, capacitive flow sensors, load sensors,
weight sensors, or torque sensors associated with elevators or
augers, or optical or other electromagnetic grain height sensors;
grain moisture sensors, such as capacitive sensors; grain loss
sensors, including impact, optical, or capacitive sensors; header
operating criteria sensors such as header height, header type, deck
plate gap, feeder speed, and reel speed sensors; separator
operating criteria sensors, such as concave clearance, rotor speed,
shoe clearance, or chaffer clearance sensors; auger sensors for
position, operation, or speed; or engine speed sensors. In an
embodiment, examples of controllers 114 that may be used with
harvesters include header operating criteria controllers for
elements such as header height, header type, deck plate gap, feeder
speed, or reel speed; separator operating criteria controllers for
features such as concave clearance, rotor speed, shoe clearance, or
chaffer clearance; or controllers for auger position, operation, or
speed.
[0108] In an embodiment, examples of sensors 112 that may be used
with grain carts include weight sensors, or sensors for auger
position, operation, or speed. In an embodiment, examples of
controllers 114 that may be used with grain carts include
controllers for auger position, operation, or speed.
[0109] In an embodiment, examples of sensors 112 and controllers
114 may be installed in unmanned aerial vehicle (UAV) apparatus or
"drones." Such sensors may include cameras with detectors effective
for any range of the electromagnetic spectrum including visible
light, infrared, ultraviolet, near-infrared (NIR), and the like;
accelerometers; altimeters; temperature sensors; humidity sensors;
pitot tube sensors or other airspeed or wind velocity sensors;
battery life sensors; or radar emitters and reflected radar energy
detection apparatus. Such controllers may include guidance or motor
control apparatus, control surface controllers, camera controllers,
or controllers programmed to turn on, operate, obtain data from,
manage and configure any of the foregoing sensors. Examples are
disclosed in U.S. patent application Ser. No. 14/831,165 and the
present disclosure assumes knowledge of that other patent
disclosure.
[0110] In an embodiment, sensors 112 and controllers 114 may be
affixed to soil sampling and measurement apparatus that is
configured or programmed to sample soil and perform soil chemistry
tests, soil moisture tests, and other tests pertaining to soil. For
example, the apparatus disclosed in U.S. Pat. Nos. 8,767,194 and
8,712,148 may be used, and the present disclosure assumes knowledge
of those patent disclosures.
[0111] In another embodiment, sensors 112 and controllers 114 may
comprise weather devices for monitoring weather conditions of
fields. For example, the apparatus disclosed in International Pat.
Application No. PCT/US2016/029609 may be used, and the present
disclosure assumes knowledge of those patent disclosures.
[0112] 2.4 Process Overview-Agronomic Model Training
[0113] In an embodiment, the agricultural intelligence computer
system 130 is programmed or configured to create an agronomic
model. In this context, an agronomic model is a data structure in
memory of the agricultural intelligence computer system 130 that
comprises field data 106, such as identification data and harvest
data for one or more fields. The agronomic model may also comprise
calculated agronomic properties which describe either conditions
which may affect the growth of one or more crops on a field, or
properties of the one or more crops, or both. Additionally, an
agronomic model may comprise recommendations based on agronomic
factors such as crop recommendations, irrigation recommendations,
planting recommendations, and harvesting recommendations. The
agronomic factors may also be used to estimate one or more crop
related results, such as agronomic yield. The agronomic yield of a
crop is an estimate of quantity of the crop that is produced, or in
some examples the revenue or profit obtained from the produced
crop.
[0114] In an embodiment, the agricultural intelligence computer
system 130 may use a preconfigured agronomic model to calculate
agronomic properties related to currently received location and
crop information for one or more fields. The preconfigured
agronomic model is based upon previously processed field data,
including but not limited to, identification data, harvest data,
fertilizer data, and weather data. The preconfigured agronomic
model may have been cross validated to ensure accuracy of the
model. Cross validation may include comparison to ground truthing
that compares predicted results with actual results on a field,
such as a comparison of precipitation estimate with a rain gauge or
sensor providing weather data at the same or nearby location or an
estimate of nitrogen content with a soil sample measurement.
[0115] FIG. 3 illustrates a programmed process by which the
agricultural intelligence computer system generates one or more
preconfigured agronomic models using field data provided by one or
more data sources. FIG. 3 may serve as an algorithm or instructions
for programming the functional elements of the agricultural
intelligence computer system 130 to perform the operations that are
now described.
[0116] At block 305, the agricultural intelligence computer system
130 is configured or programmed to implement agronomic data
preprocessing of field data received from one or more data sources.
The field data received from one or more data sources may be
preprocessed for the purpose of removing noise and distorting
effects within the agronomic data including measured outliers that
would bias received field data values. Embodiments of agronomic
data preprocessing may include, but are not limited to, removing
data values commonly associated with outlier data values, specific
measured data points that are known to unnecessarily skew other
data values, data smoothing techniques used to remove or reduce
additive or multiplicative effects from noise, and other filtering
or data derivation techniques used to provide clear distinctions
between positive and negative data inputs.
[0117] At block 310, the agricultural intelligence computer system
130 is configured or programmed to perform data subset selection
using the preprocessed field data in order to identify datasets
useful for initial agronomic model generation. The agricultural
intelligence computer system 130 may implement data subset
selection techniques including, but not limited to, a genetic
algorithm method, an all subset models method, a sequential search
method, a stepwise regression method, a particle swarm optimization
method, and an ant colony optimization method. For example, a
genetic algorithm selection technique uses an adaptive heuristic
search algorithm, based on evolutionary principles of natural
selection and genetics, to determine and evaluate datasets within
the preprocessed agronomic data.
[0118] At block 315, the agricultural intelligence computer system
130 is configured or programmed to implement field dataset
evaluation. In an embodiment, a specific field dataset is evaluated
by creating an agronomic model and using specific quality
thresholds for the created agronomic model. Agronomic models may be
compared using cross validation techniques including, but not
limited to, root mean square error of leave-one-out cross
validation (RMSECV), mean absolute error, and mean percentage
error. For example, RMSECV can cross validate agronomic models by
comparing predicted agronomic property values created by the
agronomic model against historical agronomic property values
collected and analyzed. In an embodiment, the agronomic dataset
evaluation logic is used as a feedback loop where agronomic
datasets that do not meet configured quality thresholds are used
during future data subset selection steps (block 310).
[0119] At block 320, the agricultural intelligence computer system
130 is configured or programmed to implement agronomic model
creation based upon the cross validated agronomic datasets. In an
embodiment, agronomic model creation may implement multivariate
regression techniques to create preconfigured agronomic data
models.
[0120] At block 325, the agricultural intelligence computer system
130 is configured or programmed to store the preconfigured
agronomic data models for future field data evaluation.
[0121] 2.5 Implementation Example-Hardware Overview
[0122] According to one embodiment, the techniques described herein
are implemented by one or more special-purpose computing devices.
The special-purpose computing devices may be hard-wired to perform
the techniques, or may include digital electronic devices such as
one or more application-specific integrated circuits (ASICs) or
field programmable gate arrays (FPGAs) that are persistently
programmed to perform the techniques, or may include one or more
general purpose hardware processors programmed to perform the
techniques pursuant to program instructions in firmware, memory,
other storage, or a combination. Such special-purpose computing
devices may also combine custom hard-wired logic, ASICs, or FPGAs
with custom programming to accomplish the techniques. The
special-purpose computing devices may be desktop computer systems,
portable computer systems, handheld devices, networking devices or
any other device that incorporates hard-wired and/or program logic
to implement the techniques.
[0123] For example, FIG. 4 is a block diagram that illustrates a
computer system 400 upon which an embodiment of the invention may
be implemented. Computer system 400 includes a bus 402 or other
communication mechanism for communicating information, and a
hardware processor 404 coupled with bus 402 for processing
information. Hardware processor 404 may be, for example, a general
purpose microprocessor.
[0124] Computer system 400 also includes a main memory 406, such as
a random access memory (RAM) or other dynamic storage device,
coupled to bus 402 for storing information and instructions to be
executed by processor 404. Main memory 406 also may be used for
storing temporary variables or other intermediate information
during execution of instructions to be executed by processor 404.
Such instructions, when stored in non-transitory storage media
accessible to processor 404, render computer system 400 into a
special-purpose machine that is customized to perform the
operations specified in the instructions.
[0125] Computer system 400 further includes a read only memory
(ROM) 408 or other static storage device coupled to bus 402 for
storing static information and instructions for processor 404. A
storage device 410, such as a magnetic disk, optical disk, or
solid-state drive is provided and coupled to bus 402 for storing
information and instructions.
[0126] Computer system 400 may be coupled via bus 402 to a display
412, such as a cathode ray tube (CRT), for displaying information
to a computer user. An input device 414, including alphanumeric and
other keys, is coupled to bus 402 for communicating information and
command selections to processor 404. Another type of user input
device is cursor control 416, such as a mouse, a trackball, or
cursor direction keys for communicating direction information and
command selections to processor 404 and for controlling cursor
movement on display 412. This input device typically has two
degrees of freedom in two axes, a first axis (e.g., x) and a second
axis (e.g., y), that allows the device to specify positions in a
plane.
[0127] Computer system 400 may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or
FPGAs, firmware and/or program logic which in combination with the
computer system causes or programs computer system 400 to be a
special-purpose machine. According to one embodiment, the
techniques herein are performed by computer system 400 in response
to processor 404 executing one or more sequences of one or more
instructions contained in main memory 406. Such instructions may be
read into main memory 406 from another storage medium, such as
storage device 410. Execution of the sequences of instructions
contained in main memory 406 causes processor 404 to perform the
process steps described herein. In alternative embodiments,
hard-wired circuitry may be used in place of or in combination with
software instructions.
[0128] The term "storage media" as used herein refers to any
non-transitory media that store data and/or instructions that cause
a machine to operate in a specific fashion. Such storage media may
comprise non-volatile media and/or volatile media. Non-volatile
media includes, for example, optical disks, magnetic disks, or
solid-state drives, such as storage device 410. Volatile media
includes dynamic memory, such as main memory 406. Common forms of
storage media include, for example, a floppy disk, a flexible disk,
hard disk, solid-state drive, magnetic tape, or any other magnetic
data storage medium, a CD-ROM, any other optical data storage
medium, any physical medium with patterns of holes, a RAM, a PROM,
and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or
cartridge.
[0129] Storage media is distinct from but may be used in
conjunction with transmission media. Transmission media
participates in transferring information between storage media. For
example, transmission media includes coaxial cables, copper wire
and fiber optics, including the wires that comprise bus 402.
Transmission media can also take the form of acoustic or light
waves, such as those generated during radio-wave and infra-red data
communications.
[0130] Various forms of media may be involved in carrying one or
more sequences of one or more instructions to processor 404 for
execution. For example, the instructions may initially be carried
on a magnetic disk or solid-state drive of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem. A
modem local to computer system 400 can receive the data on the
telephone line and use an infra-red transmitter to convert the data
to an infra-red signal. An infra-red detector can receive the data
carried in the infra-red signal and appropriate circuitry can place
the data on bus 402. Bus 402 carries the data to main memory 406,
from which processor 404 retrieves and executes the instructions.
The instructions received by main memory 406 may optionally be
stored on storage device 410 either before or after execution by
processor 404.
[0131] Computer system 400 also includes a communication interface
418 coupled to bus 402. Communication interface 418 provides a
two-way data communication coupling to a network link 420 that is
connected to a local network 422. For example, communication
interface 418 may be an integrated services digital network (ISDN)
card, cable modem, satellite modem, or a modem to provide a data
communication connection to a corresponding type of telephone line.
As another example, communication interface 418 may be a local area
network (LAN) card to provide a data communication connection to a
compatible LAN. Wireless links may also be implemented. In any such
implementation, communication interface 418 sends and receives
electrical, electromagnetic or optical signals that carry digital
data streams representing various types of information.
[0132] Network link 420 typically provides data communication
through one or more networks to other data devices. For example,
network link 420 may provide a connection through local network 422
to a host computer 424 or to data equipment operated by an Internet
Service Provider (ISP) 426. ISP 426 in turn provides data
communication services through the world wide packet data
communication network now commonly referred to as the "Internet"
428. Local network 422 and Internet 428 both use electrical,
electromagnetic or optical signals that carry digital data streams.
The signals through the various networks and the signals on network
link 420 and through communication interface 418, which carry the
digital data to and from computer system 400, are example forms of
transmission media.
[0133] Computer system 400 can send messages and receive data,
including program code, through the network(s), network link 420
and communication interface 418. In the Internet example, a server
430 might transmit a requested code for an application program
through Internet 428, ISP 426, local network 422 and communication
interface 418.
[0134] The received code may be executed by processor 404 as it is
received, and/or stored in storage device 410, or other
non-volatile storage for later execution.
[0135] 3. Identifying Management Zones Based on Historical Yield
DATA
[0136] A management zone usually includes one or more subregions
within an agricultural field that have similar limiting factors
influencing yields of crops harvested from the subregions.
Subregions that belong to the same management zone can usually be
managed uniformly.
[0137] In an embodiment, management zones are created automatically
using specialized processors. An automatic creation of the zones
may include several processing steps, some of which are performed
either sequentially or in parallel.
[0138] Creating management zones may start with receiving yield
data representing yields of crops harvested from an agricultural
field. The yield data may include historical, sub-field yield maps,
or any other types of data representing the yield information.
[0139] In an embodiment, received yield data is preprocessed. In
some situations, preprocessing is optional; in other situations,
preprocessing is mandatory. Preprocessing may be performed to
correct various problems with the yield data. This may include
identifying and removing outliers, determining whether any yield
data for the field is missing, generating substitute values for the
missing data, or correcting errors in the received data.
[0140] Received yield data may also be normalized. This may include
converting the received yield data to data that is within the same
data range. Yield data may also be processed by applying smoothing
functions to the data. Smoothing of the data may help to eliminate
outliers, fill in the missing data records, and correct inaccurate
observations.
[0141] Data that has been preprocessed, normalized and smoothed may
be used to delineate management zones. Delineation may include
classifying the field into different productivity regions. Through
this process, one or more management zones may be created, and each
of the zones may be identified as different from the remaining
zones because it produces different yield of crops than other
zones.
[0142] In an embodiment, a set of management zones is
post-processed. Post-processing may include removing undesirably
fragmented or small zones, and merging them with other, larger
zones.
[0143] In an embodiment, a set of first merged management zones is
used to automatically control a computer control system of one or
more of seeding, irrigation, nitrogen application, and harvesting
operations.
[0144] In an embodiment, a process of creating management zones is
fully automated and is executed fast enough to operate in real
time. The process allows creating management zones that are
spatially contiguous and have reasonable sizes.
[0145] The process may also provide a quality measure to tune
various parameters used in the delineation steps. A quality measure
may be configured to mathematically model and balance the
compactness of yield data within each zone and the separation of
yield data between different zones. Using the quality measure
allows generating management zones that have not only homogeneous
yields within each zone and year, but also have different yield
patterns between each other. The differentiation of the yield
patterns among different zones allows customizing the cultivations
practices for each individual zone.
[0146] 3.1 Management Zones Creating Pipeline
[0147] In the context of precision agriculture, management zones
are contiguous subregions of a field with a relatively homogeneous
combination of yield limiting factors, such that the optimal rate
of a specific crop input or management practice is reasonably
uniform within each zone.
[0148] One of the objectives for creating the zones is to divide
the entire agricultural field into different productivity regions
with distinctive spatial-temporal yielding behaviors. Creating, or
identifying, such zones may help and guide the crops growers by
providing them with recommendations for seeding rate selections
based on the monotonic relationship found between observed
yields.
[0149] In an embodiment, management zones are delineated within an
agricultural field using a management zone creating pipeline.
[0150] FIG. 7 depicts an example embodiment of a management zone
creation pipeline. FIG. 7 represents programmed processing steps
and may represent an algorithm for use in programming the
instructions previously discussed for FIG. 1. Management zone
creating pipeline 701 includes processing blocks for actions
performed sequentially, in parallel or that are optional as further
described in this section.
[0151] Block 702 represents program instructions for receiving
data. In block 702, yield data is received; for example, system 130
(FIG. 1) receives yield data as part of the field data 106. The
yield data may include historical yield maps at the field level or
sub field level. The maps represent spatio-temporal yielding
patterns for the sub-fields and are used to classify a field into
regions with distinctive or different productivity potentials.
[0152] Yield data may be received from different sources such as
research partners (RP), agencies, organizations, growers and
others. Yield data may include information about yield of crops
harvested from an agricultural field within one year or multiple
years. In an embodiment, yield data may also include metadata such
as a field boundary, a field size, and a location of each sub-field
within the field.
[0153] Blocks 704, 706 and 708 represent program instructions for
preprocessing, density processing and data smoothing of the
received yield data. Preprocessing at block 704 may be performed,
for example, because some of the yield data observations for a
field may be collected outside of corresponding field boundaries.
The preprocessing may also be recommended when the yield data is
provided from a field on which multiple crops were planted in the
same season.
[0154] Instructions for blocks 704, 706 and 708 may be executed
selectively, optionally, sequentially, or in parallel. The manner
in which the tasks are selected can vary based on the
implementation and the quality of received yield data. For example,
some of the received yield data may need preprocessing but not
smoothing. Other data may need only density processing. Selecting
one or more of blocks 704, 706, 708 may be based on manual or
machine inspection of the received yield data as part of block
702.
[0155] In block 704, preprocessing may include identifying and
removing yield observations that are outside of their corresponding
field boundaries. Preprocessing may also include identifying, and
removing, the yield observations if multiple crops were planted
within the field in the same season. Other examples of yield data
preprocessing are described in connection with FIG. 8.
[0156] In block 706, yield data is processed based on data density.
Data density processing may be performed to normalize the yield
data across different crops and fields. In an embodiment, data
density processing comprises using an empirical cumulative
distribution function (ECDF) transformation, which may be performed
on the yield records for each field and year so that the
transformed yield data is within a certain range across different
crops and fields. For example, the ECDF may be applied to the
received yield data to transform the data into transformed yield
data in the range of [0, 1]. Once the yield data is transformed,
the transformed yield data may be compared across different years
and crops, such as corn, soy, or wheat.
[0157] In block 708, yield data is processed by smoothing. Data
smoothing may include testing whether any yield data records are
missing, whether the yield data records need to be further
smoothed, or whether certain yield data records need to be removed
or interpolated.
[0158] In block 710, yield data is used to delineate a set of
management zones for an agricultural field. A set of delineated
management zones may be represented using stored digital zone data,
and created by applying centroid-based approaches, such as a
K-means approach, or a fuzzy C-means approach. Details of these
approaches are described further herein in connection with FIG. 8.
The process executed in block 710 may be repeated, as depicted by
arrow 712, one or more times until the quality of the created
management zones is satisfactory. The process may be repeated using
different criteria, different parameters, or different parameter
values.
[0159] One criterion that may be used to determine a quality of
management zones is compactness. Zones that are generated using a
good management zone delineation approach are compact. Generating
compact zones involves maximizing the within-zone homogeneity.
There should also be a well-defined separation between different
zones to ensure that the created zones actually require different
management practices. The compactness and separation of the created
management zones may be evaluated by a visual assessment by either
directly overlapping the delineated zones with the yield maps, or
by plotting the distribution of yield values in each zone and year.
The compactness and separation may also be evaluated by a
quantitative assessment which defines numeric measures to
accurately quantify the compactness and separation of yield
observations in the delineated zones. Details of determining a
quality of the created management zones as represented in zone data
are described further herein in connection with FIG. 8.
[0160] In block 714, a set of delineated management zones is
analyzed to determine whether some of the zones may be merged. For
example, a set of delineated management zones may be analyzed to
identify small zones and to determine whether the small zones may
be merged with neighboring larger zones.
[0161] Small zones may be identified automatically by a computer
system, or manually by a user of the computer system. For example,
the computer system may display information about the set of first
management zones to a crop grower in a graphical user interface
that is programmed with widgets or controls to allow the grower to
remove undesirable fragmented small zones, or to merge the
fragmented small zones with larger zones. Merging of zones results
in obtaining a set of merged management zones. Details about
merging the management zones are described further herein in
connection with FIG. 8.
[0162] If small zones cannot be identified in a set of delineated
management zones, then the set of delineated management zones is
provided to block 718, as indicated by 724, and thus bypassing
block 714.
[0163] The process executed in block 714 may be repeated, as
depicted by arrow 716, one or more times until no small zones are
identified in the set of management zones. The process may be
repeated using different criteria, different parameters, or
different parameter values. Details are described further herein in
connection with FIG. 8.
[0164] In block 718, a set of management zones is post-processed.
Post-processing of the management zones may include eliminating the
zones that are fragmented or unusable.
[0165] The process executed in block 718 may be repeated, as
depicted using arrow 720, one or more times until a quality of
created management zones is satisfactory. The process may be
repeated using different criteria, different parameters, or
different parameter values. Details of determining the quality of
the created management zones are described further herein in
connection with FIG. 8.
[0166] In an embodiment, metadata about the created management
zones is stored. Furthermore, a test may be performed to determine
whether the process of delineating management zones needs to be
repeated. If the delineation process is to be repeated, then, as
indicated using arrow 722, the delineating of the management zones
is repeated in block 710.
[0167] 3.2 Creating Management Zones
[0168] In an embodiment, a management zones creating process is
programmed to automatically delineate management zones within an
agricultural field based on any type of yield maps.
[0169] 3.2.1 Yield Data
[0170] FIG. 8 depicts a method for creating management zones for an
agricultural field. In step 810, yield data is received. As
described for FIG. 7, the yield data represents yields of crops
that have been harvested from an agricultural field. Yield data may
include historical, sub-field yield maps that represent
spatio-temporal yielding patterns for the sub-fields. Yield data
may be provided from different sources, such as research partners,
agricultural agencies or organizations, growers, governmental
agencies, and others. Yield data may include information about
yields of crops harvested from an agricultural field within one
year or within several years. In an embodiment, yield data may also
include additional information such as a field boundary, a field
size, and a location of each sub-field within the field.
[0171] 3.2.2 Preprocessing of Received Data
[0172] In step 820, the received yield data is preprocessed.
Preprocessing of the yield data may be performed to reduce noise
observations from the yield observations, impute missing yield
values to standardize the zone delineation step, and so forth. In
an embodiment, received yield data is preprocessed to correct
certain issues with the data. The preprocessing may include various
types of data cleaning and filtering.
[0173] In an embodiment, preprocessing of yield data includes
removing outliers from the yield data. Yield data may include
sub-field yield observations that consist of various contaminations
caused by unavoidable errors introduced by the way the crops are
harvested, or by the way the yield data is collected or recorded.
Removing of such errors or outliers effectively results in
decontaminating the yield data.
[0174] In an embodiment, yield data received for an agricultural
field is analyzed to identify yield maps that correspond to
multiple crops planted in the same season and in the same field. If
such maps are found, then such maps may be neglected from further
processing.
[0175] In an embodiment, received yield data is analyzed to
identify any yield records that are outside of the field
boundaries. If such yield records are found, then such records may
be removed.
[0176] In an embodiment, received yield data is analyzed to
identify yield records that contain absolute zeros. Those records
may be considered as missing values, and thus may be ignored in
further processing. Missing records may be due to the nature of
their cleaning procedures. However, in some cases, those records
may be subjected to a further analysis and validation, through
which it may be determined that the records are valid.
[0177] In an embodiment, received yield data is analyzed to
identify any yield maps that has more than 70% missing data
records. Such yield maps may be removed from the yield data.
[0178] In an embodiment, received yield data is analyzed to
determine whether less than two years of yield maps for a field are
provided. If less than two years of yield maps for a field are
provided, then the yield maps are not included in the zone
delineation.
[0179] In embodiment, additional data preprocessing and filtering
are performed on yield data. An example is adjusting to account for
grain moisture. Grain moisture adjustment allows correcting the
yield data records for some fields and years that were harvested at
a moisture level that is other than a standard moisture level such
as 15.5% moisture.
[0180] The additional processing may also be directed to correcting
yield productivity data caused when the experimental yield data is
provided. The additional processing may include correcting of yield
data if the data was pre-smoothed by the data provider using
undesired algorithms or parameters. This type of additional
processing is recommended to reduce the effect of improperly
smoothed yield data on the results of the management zones
creation.
[0181] In an embodiment, yield records in the received yield data
are transformed from Latitude-Longitude coordinates to Universal
Transverse Mercator (UTM) coordinates, and mapped onto a grid of
for example, 10 m.times.10 m grid defined for the field. The
mapping allows standardization of locations of the yield records
within the field, which assists management zone creation.
[0182] 3.2.3 Imputing Missing Yield Values
[0183] In an embodiment, either raw or processed yield data for a
field is plotted on a virtual geometric digital grid for the field.
If no yield data record is plotted on a particular grid, then yield
data records from neighboring grids is retrieved and used to
determine a particular yield data record for the particular
grid.
[0184] A particular yield data record for a particular grid may be
determined using programmed instructions that are configured to
determine neighboring yield data records from neighboring grids,
determine weights for each of the neighboring yield data records,
compute a weighted sum of the neighboring yield data records, and
use the computed weighted sum as the particular yield data. The
weights may be inversely proportional to a distance between the
particular grid, or a center of the particular grid, and respective
neighboring grids, or their centers. If a grid size is for example
10 m, then a maximum distance may be set to for example, 20 m, and
so forth. Using this approach, the dispersed missing grid yield
data may be interpolated using yield data records from the
neighboring grids.
[0185] In some situations, yield data for an agricultural field may
be incomplete or inaccurate. For example, some yield data may
include no valid yield data records for some sub-fields. This may
be due to some systematic collection errors or improper data
cleaning performed by providers of the yield data. Furthermore,
there may be inconsistency in the yield data for the same field,
but for different years. For example, when viewing multiple years
of yield maps for the same field, the maps for some years may miss
some values for a particular sub-field, while other maps for other
years may have valid values for the particular sub-field.
[0186] Some yield data may be missing data records for certain
locations in an agricultural field. This may be caused by for
example, an irregular location of a field. In some situations,
yield data may contain no yield data records for not just
individual locations, but also for a group of locations. This may
be caused by problems with the data collection equipment, data
corruption, and the like. In these situations, interpolating the
missing data may be difficult. However, in other cases, the missing
data may be obtained by using data interpolation approaches.
[0187] In an embodiment, missing yield data records are determined
using a model-based approach. This approach integrates
spatiotemporal modeling of the yield maps with subsequent zone
delineation algorithms.
[0188] In an embodiment, missing yield data records are determined
using imputation to supply missing values and ensure a delineation
algorithm is provided a complete set of yield data. One of the
benefits of this approach is its simplicity and robustness when
applied to diverse field conditions. In this approach, it is
assumed that missing yield observations have not been yet spatially
imputed because of their long distances to other observed
locations, or grids, identified in the same yield map.
[0189] In an embodiment, imputation is performed on a plurality of
yield data observations. Let Y.sub.i,t be the yield data
observation at a location i and a year t. To impute a missing
Y.sub.i,t, an average of Y.sub.i,t' is computed for those years t'
in which yield data observations are available. This is expressed
using the following equation:
^ i , t = t ' .di-elect cons. A i i , t ' ( t ' .di-elect cons. A i
) t ' .di-elect cons. A i ( t ' .di-elect cons. A i ) ( 1 )
##EQU00001##
where A.sub.i is the set of years with observed yield values at
location i, and 1(.) is an indicator function which equals to one
if the condition is (.) is true, and equals to zero otherwise.
[0190] Equation (1) may be used to determine a missing Y.sub.i,t by
cross-imputing yield data observations for a location i and years
other than a year t.
[0191] After the cross imputation of the missing yield data is
completed, any grid in the field with observed yield values in at
least one of the yield maps will have a full set of yield values in
the yield maps for all years.
[0192] However, if a grid does not have a yield observation in any
of the yield maps, then after performing the data imputation
approach, the grid will still be missing a yield data record.
Furthermore, applying a management zone delineation approach to the
data in such a grid will not result in generating any zone label
for the grid.
[0193] 3.2.4 Empirical Cumulative Density Transformation
[0194] ECDF transformation may be used to transform the yield data
into the transformed yield data. Application of ECDF to the yield
data may cause transforming the yield data records to transformed
yield data records, each of which falls within a particular range.
Applying ECDF to the yield data causes normalizing the yield data
so that the normalized yield data is comparable across different
years and crops, such as corn, soy, and wheat.
[0195] In step 830, preprocessed data is transformed to result in
creating and storing digitally transformed yield data. Transformed
yield data may be generated by applying ECDF transformation to
yield data records for each field and year to transform the data
record values to a certain range of values. For example, ECDF
transformation may be applied to yield data records to transform
the data records to be within the range of [0, 1].
[0196] 3.2.5 Spatial Smoothing
[0197] Data smoothing may be performed on either raw yield data or
processed yield data. That depends on a quality of the received raw
yield data and on the implementation.
[0198] In an embodiment, a spatial smoothing is performed to remove
measurement noises in the raw yield observations and reduce
unnecessary fragmentation of delineated management zones. The
spatial smoothing may be performed using approaches such as a
kernel-smoother, or a stationary Gaussian process.
[0199] A kernel smoother is a statistical technique for estimating
a function by using its noise observations when no parametric model
for the function is known. The resulting estimated function is
usually smooth. The estimated smooth function may be used to remove
the noise observations from a set of observations, such as the
yield data.
[0200] In an embodiment, kernel smoothers that are reliable and
useful nonparametric estimators are selected to perform a spatial
smoothing of yield data. Examples of the kernel smoothers that can
be used to smooth the yield data include a Gaussian kernel, an
inverse distance weighting kernel, a rectangular kernel, a
triangular kernel, a Bi-Square kernel, a tri-cube kernel, and a
tri-weight kernel. Besides their standard parameterization, all of
them are also equipped with a scale parameter h and a span
parameter H such that the distance between yield data observations
may be scaled and the observations that are more than H away from
the destination point may be omitted in the smoothing process.
[0201] Referring again to FIG. 8, in step 840, smooth transformed
yield data is generated by smoothing the transformed yield data.
Smooth transformed yield data may be generated using any of the
kernel estimators described above.
[0202] An example kernel estimator for determining a missing
Y.sub.i,t yield data observation at a location i and a year t may
be expressed using the following equation:
.sub.i,t={.SIGMA..sub.jK(d.sub.i,j)Y.sub.j,t}/{.SIGMA..sub.jK(d.sub.i,j-
)} (2)
where K is a kernel function selected from the examples described
above, Y.sub.j,t is the yield data observation at a location j and
a year t, and d.sub.i,j is the distance between a location i and a
location j.
[0203] In an embodiment, a Gaussian kernel smoother is used in
equation (2). In a Gaussian kernel smoother, parameters h and H can
be either selected empirically based on experience with the data,
or quantitatively optimized by cross validations.
[0204] In step 850, a test is performed to determine whether the
obtained yield data is acceptable for subjecting it to a management
zones delineation process. The test may include testing whether any
yield data records are still missing, whether the yield data
records need to be further smoothed, or whether certain yield data
records need to be removed or interpolated.
[0205] If in step 850 it is determined that the obtained yield data
is acceptable, then step 860 is performed. Otherwise, depending on
the outcome of the test, any of steps 820, 830 or 840 is performed.
For example, if obtained yield data needs to be further
preprocessed, then step 820 is performed. If the obtained yield
data needs to be transformed again, then step 830 is performed. If
the obtained yield data needs to be smoothed again, then step 840
is performed. It is also possible that two or three steps of steps
820, 830 and 840 are performed.
[0206] 3.2.6 Identifying Management Zones
[0207] In an embodiment, a management zones delineation process is
performed for different values of a management class count. A
management class refers to areas in a field that have relatively
homogeneous yield limiting factors, but that are not restricted to
be spatially contiguous. Concept-wise, several management zones
which are spatially separated from each other could belong to the
same management class and could be operated in the same manner.
Even though a management zone and a management class are often used
interchangeably, these two terms are distinguishable, especially in
reference to the presented zone creation approach.
[0208] In an embodiment, a delineation process is executed first
for a first count value of the management class count, and if the
quality of the generated zones is for some reason inadequate, then
the process may be repeated for a second count value of the class
count, and so forth.
[0209] Referring again to FIG. 8, in step 860, a first count value
for a management classes count of a plurality of management classes
is determined. Selecting a first count value for the management
classes may include selecting an optimal number of management
classes for creating the zones.
[0210] An optimal number of management classes may be found using a
variety of approaches. According to one approach, an optimal number
of management classes is selected by using all years of training
yield maps at once. In this approach, a clustering algorithm is
applied to the smoothed training yield maps with different number
of classes and for each class. Then a training zone-quality measure
for each class numbers is determined and used to identify an
optimal number of classes. An example of the measure is a measure
that checks for compactness and separation of classes and for each
class. The compactness and separation of the classes and for each
class are examples of the qualities that are considered in
determining the final zones.
[0211] According to another approach, an optimal number of
management classes is selected by carrying out a leave-one-year-out
cross-validation approach for training yield maps.
[0212] Once a first count value is determined for a count of a
plurality of classes, a first set of management zones is generated
in step 870. The first set of management zones may be generated,
for example, using a management zones delineation process that is
performed using either a clustering approach or a region merging
approach. Examples of a clustering approach may include
centroid-based multivariate clustering approaches, such as a
K-means approach and a fuzzy C-means approach. Examples of a region
merging approaches may include agglomerative region merging
approaches, such as a hierarchical region-based segmentation
approach.
[0213] 3.2.6.1 K-Means Approach
[0214] In an embodiment, a management zones delineation process is
implemented using a K-means approach. K-means approach aims to
partition a set of yield data observations into k clusters in which
each observation belongs to the cluster with the nearest mean. Let
assume that Y.sub.i,t is the yield observation at location i in
year t where i belongs to a set {1, 2, . . . T}. Furthermore, let
assume that Y.sub.i={Y.sub.i,1, Y.sub.i,2. . . , Y.sub.i,T} for any
i=1, 2, . . . , n. Then, for a given k from a set {1, 2 . . . n},
the K-means approach aims to find the k sets S={S.sub.1, S.sub.2 .
. . S.sub.k} such that the within-set sum of squares is minimized.
This may be expressed using the following equation:
min S i = 1 n j = 1 k ( i - .mu. j ) 2 ( i .di-elect cons. S j ) (
3 ) so that : j = 1 k ( i .di-elect cons. S j ) = 1 , .A-inverted.
i ; and .mu. j = i = 1 n i ( i .di-elect cons. S j ) i = 1 n ( i
.di-elect cons. S j ) , .A-inverted. j . ( 4 ) ##EQU00002##
[0215] One of the advantages of using the K-means approach in the
management zone delineation process is its simplicity. One of the
disadvantages of using the K-means approach is that it does not
consider spatial locations of the yield data observations within
the field. As a result, a direct output from K-means clustering is
the management class labels of each pixel i, and some additional
steps may be needed to identify spatially contiguous zones.
Moreover, it is recommended to use well preprocessed yield maps
before using the K-means approach. If the yield maps are
insufficiently preprocessed, then the results produced by the
K-means approach may include many fragmented small zones.
[0216] The k that belongs to a set {1, 2 . . . n} corresponds to
the first count value, and represents a number of management
classes described above. The k parameter is also referred to as a
tuning parameter in the K-means approach. When k increases, the
within-set sum of squares in equation (3) decreases for the same
set of data, and the within-class compactness increases. However,
when k is increased above a certain threshold, the K-means approach
may lead to over-segmentation of the field, and the within-class
compactness may need to be balanced.
[0217] 3.2.6.2 Fuzzy C-Means Approach
[0218] In an embodiment, a management zones delineation process is
implemented using a fuzzy C-means approach. Fuzzy C-means, also
called fuzzy K-means, is a fuzzy extension of the K-means approach.
In the fuzzy C-means approach, instead of assigning a hard label to
each observation Y.sub.i, each observation may be assigned to more
than one cluster with different membership levels.
[0219] Let assume that for a given k, from a set {1, 2 . . . n},
the fuzzy C-means algorithm aims to find the centers C={C.sub.1,
C.sub.2, . . . , C.sub.k} and a n.times.k membership matrix
U=[u.sub.ij], where u.sub.i,j belongs to a set [0, 1] such that the
following weighted sum of squares is minimized. This may be
expressed using the following equation:
min C , U i = 1 n j = 1 k u i , j m i - .mu. j 2 ( 5 ) where : 0
.ltoreq. u i , j .ltoreq. 1 , .A-inverted. i , j ; j = 1 k u i , j
= 1 , .A-inverted. i ; 0 < i = 1 n u i , j < n , .A-inverted.
j . ( 6 ) ##EQU00003##
[0220] In the equation (5), .parallel...parallel. stands for norm,
such as the Euclidean norm. The m is the fuzzifier with m.gtoreq.1,
and determines the level of cluster fuzziness. In general, the
larger m results in fuzzier clusters and in the lower limit when
m=1, and thus the fuzzy C-means approach degenerates to K-means
approach. When there is no strong experimentation or domain
knowledge, the common choice for m is 2.
[0221] Similar to K-means approach, the direct optimization for
equation (5) may be difficult, and an iterative approach is often
carried out to perform the optimization step by step. For example,
C and U may satisfy the following relationship to be the optimal
solution of equation (5):
C j = i = 1 n u i , j m i i = 1 n u i , j m , .A-inverted. j ; ( 7
) where u i , j = 1 j = 1 k ( i - C j i - C k ) 2 m - 1 ,
.A-inverted. i , j . ( 8 ) ##EQU00004##
[0222] In an embodiment, an iterative algorithm starts with a
randomly initialized membership matrix U=[u.sub.i,j], and then
repeatedly updates the cluster centers C and the membership matrix
U based on equations (7)-(8), respectively, until their values
converge. After that, the management class label for a pixel i may
be determined as:
arg max.sub.j=1, . . . ,k u.sub.i,j. (9)
[0223] In an embodiment, compared to the K-means approach, the
computational complexity of the fuzzy C-means approach is slightly
higher. However, upon assigning crisp management class labels at
the end, the outputs generated by the K-means approach and the
outputs generated by the fuzzy C-means approach may be very
similar.
[0224] 3.2.6.3 Region Merging Approach
[0225] In an embodiment, a management zones delineation process is
programmed to use hierarchical region-based segmentation. In this
approach, two zones are neighboring to each other if, and only if,
at least one pair of pixels between the two zones are neighbors
based on the nearest 4-neighbor rule.
[0226] In an embodiment, a dissimilarity score for neighboring
zones is computed. When calculating the dissimilarity score for any
two neighboring zones, a modified complete-link distance measure of
their yield observations is adopted. For example, let S.sub.A and
S.sub.B are the sets of pixels belonging to zone A and zone B,
respectively. The dissimilarity score between zone A and zone B may
be calculated as follows:
d.sub.A,B=Quantile({mean.sub.t|Y.sub.i,t-Y.sub.j,t|, s.t.
i.di-elect cons.S.sub.A&j.di-elect cons.S.sub.B}, 95%) (10)
[0227] Therefore, if the dissimilarity score between two zones A
and B is d.sub.A,B, then it means that 95% of the between-zone
pairs of pixels have a difference no larger than d.sub.A,B absolute
difference in their yield observations on average over all
years.
[0228] In an embodiment, a hierarchical region-based segmentation
approach is implemented using code instructions shown in Table 1,
below:
TABLE-US-00001 TABLE 1 Algorithm 1: The region merging algorithm
for zone creation Data: yield maps, dissimilarity threshold
.di-elect cons. Result: zone labels for each pixel 1 begin 2 |
Initialization: each pixel is one zone ; 3 | while more than one
zone do 4 | | calculate the dissimilarity score between each pair
of | | neighboring zones based on their yield observations ; 5 | |
let d = the minimum dissimilarity score; 6 | | if d .ltoreq.
.di-elect cons. then 7 | | | merge the most similar pair of zones;
8 | | | update proximity relation of the zones; 9 | | else 10 | | |
return the current zone labels ; 11 | | end 12 | end 13 end
[0229] Table 2 below summarizes and compares three management zones
delineating approaches described above.
TABLE-US-00002 TABLE 2 Tuning Algorithm Input Output Parameter Note
K-means yield class k(k .di-elect cons. {1, 2, . . . , n}) computa-
observations labels tionally {Y.sub.i,t} fastest Fuzzy yield class
k(k .di-elect cons. {1, 2, . . . , n}) similar C-means observations
labels output {Y.sub.i,t} as K-means but slower Region yield zone
e(e .di-elect cons. [0, 1]) computa- merging observations labels
tionally {Y.sub.i,t}; slowest, pixel spatial finer tuning, location
spatially contiguous zones
[0230] One of the advantages of the region merging approach is that
it utilizes a spatial location of the yield observations when
creating the management zones. The approach is expected to generate
spatially contiguous zones naturally unless the dissimilarity
threshold is set too strict or the yield maps are too rough. In
addition, as the dissimilarity threshold e is a continuous tuning
parameter, as opposed to k, which takes only positive integers in
K-means or fuzzy C-means, the hierarchical region merging algorithm
may have more flexibility to fine tune the resulting zone
delineation, and satisfy the diverse needs from different
growers.
[0231] Another advantage of the region merging approach is that the
region merging algorithm generates zone labels directly without
class labels.
[0232] However, although the region merging approach may not
include an additional processing to present management zones, some
post processing of the zone properties may be recommended.
[0233] In step 880, a test is performed to determine whether a
count of management classes is to be changed. If the count is to be
changed, then step 890 is performed. Otherwise, step 895 is
performed. A count of management classes corresponds to a tuning
parameter described above.
[0234] In step 890, a second count value for a count of management
classes from among a plurality of management classes is determined,
and steps 870-880 are repeated for the second count value.
[0235] 3.2.7 Post Processing
[0236] In an embodiment, a set of management zones is
post-processed. Post-processing of the management zones may be
performed for various reasons. Post-processing may be performed for
example, to clean small isolated zones to make sure all zones are
spatially contiguous and have reasonable sizes. Small isolated
zones are the zones that may be too small to cause a crop grower to
change his agricultural practices. Small isolated zones are also
referred to as fragmented zones. Post-processing may also be
performed to remove small isolated zones. Even with spatial
smoothing of the yield maps during the yield data preprocessing
phase, the set of management zones may include small isolated zones
that may be difficult to manage individually.
[0237] In an embodiment, a test is performed to determine whether a
size of a zone is smaller than a user-defined threshold s. If the
size of the zone is smaller than the threshold s, then the zone is
merged with its most similar neighboring large zone that is larger
than the small zone. The zone/class label of the large zone may be
assigned to the merged zone.
[0238] If the class labels are obtained from the K-means or fuzzy
C-means approaches, however, then two additional steps may be
performed. For example, before zone cleaning, a set of zones may be
constructed based on the class labels and the spatial location of
pixels so that the size and neighboring zones of each management
zone may be identified. After the zone cleaning, the class labels
may be recovered from the constructed set, and the additional zone
merges may be performed.
[0239] FIG. 9 depicts a method for management zones
post-processing. In step 910, a test is performed to determine if
any small zone next to a large zone is present in a set of
management zones.
[0240] If in step 920 it is determined that no small zone next to a
large zone is present in a set of management zones, then in step
930, the set of management zones is stored. The set of management
zones may be stored in a storage device, a memory unit, a cloud
storage service, or any other storage device. The set of management
zones may be used to determine seeding recommendations for growers,
for research purposes, and for providing information to other
agencies.
[0241] However, if in step 920 it is determined that at least one
small zone is present next to a large zone in a set of management
zones, then the small zones are merged with their respective large
zones.
[0242] A merging of the zones may be performed for each identified
small zone, as indicated in steps 950-960. Once all identified
small zones are merged with their respective large zones, in step
970 the resulting set of merged management zones is stored. The set
of merged management zones may be stored in a storage device, a
memory unit, a cloud storage service, or any other storage device.
The set of management zones may be used to determine seeding
recommendations for growers, for research purposes, and for
providing information to other agencies.
[0243] 3.3 Performance Considerations
[0244] Accuracy of the approach for delineating management zones in
an agricultural field depends on a variety of factors. For example,
assuming that the quality of the yield maps is comparable from year
to year, the quality and accuracy of the management zones
delineation increases proportionally to the number of yield maps
from different years provided to the system. Hence, for a given
field, the more years of yield maps are provided, the higher the
quality and accuracy of management zones delineation may be.
[0245] 3.4 Usefulness of Management Zones Delineation
[0246] Using the techniques described herein, a computer can
determine a plurality of management zones based on digital data
representing historical yields harvested from an agricultural
field. The techniques herein can enable computers to determine the
plurality of contiguous regions within an agricultural field that
have similar limiting factors influencing the harvested yields of
crops. The presented techniques can also enable the agricultural
intelligence computing system to automatically determine the
regions that belong to the same management zone and to generate
recommendations for crop growers with respect to seeding,
irrigation, application of fertilizers such as nitrogen, and/or
harvesting.
[0247] Furthermore, the presented techniques can enable the
agricultural intelligence computing system to save computational
resources, such as data storage, computing power, and computer
memory of the system, by implementing a programmable pipeline
configured to automatically determine management zones for a field
based on digital data. The programmable pipeline can automatically
generate recommendations and alerts for farmers, insurance
companies, and researchers, thereby allowing for a more effective
agricultural management in the seeding schedules, operations of
agricultural equipment, and application of chemicals to fields,
protection of crops and other tangible steps in the management of
agricultural field. Management zones created based on historical
yield data may be particularly useful in certain agricultural
practices, such as selecting a seeding rate. For example,
information about the created management zones may be used to
generate recommendations for crop growers. The recommendations may
pertain to seed and seeding selections. Selecting a recommended
seeding rate based on the identified management zones may be very
helpful in increasing harvested yields.
[0248] 3.5 Extensions
[0249] In an embodiment, a process of delineating management zones
within an agricultural field is enhanced by taking into
consideration not only the historical yield maps, but also weather
forecast information. In this approach, the weather information may
be used to provide explanations for inconsistencies in yield
observations recoded in the historical yield maps.
[0250] A process of delineating management zones within a field may
also be enhanced by providing information about soil properties and
topographical properties of the field to the process. Usually,
permanent soil and topographical properties play more important
roles in determining sub-field yield variability than those
transient factors such as weather.
[0251] Accuracy of the information about management zones may be
further improved if the growers can provide sufficient historical
yield data or sub-field yield maps to the system. Furthermore,
accuracy of the information about management zones may be improved
if the growers can provide the historical yield data in the
required format and to the required input site.
* * * * *