U.S. patent application number 12/911046 was filed with the patent office on 2012-04-26 for wide-area agricultural monitoring and prediction.
This patent application is currently assigned to Trimble Navigation Limited. Invention is credited to Robert J. Lindores, Ted E. Mayfield, Morrison Ulman.
Application Number | 20120101784 12/911046 |
Document ID | / |
Family ID | 45973703 |
Filed Date | 2012-04-26 |
United States Patent
Application |
20120101784 |
Kind Code |
A1 |
Lindores; Robert J. ; et
al. |
April 26, 2012 |
Wide-area agricultural monitoring and prediction
Abstract
Ground-based measurements of agricultural metrics such as NDVI
are used to calibrate wide-area aerial measurements of the same
metrics. Calibrated wide-area data may then be used as an input to
a field prescription processor.
Inventors: |
Lindores; Robert J.;
(Boulder, CO) ; Mayfield; Ted E.; (Ukiah, CA)
; Ulman; Morrison; (Mountain View, CA) |
Assignee: |
Trimble Navigation Limited
Sunnyvale
CA
|
Family ID: |
45973703 |
Appl. No.: |
12/911046 |
Filed: |
October 25, 2010 |
Current U.S.
Class: |
703/2 ;
702/85 |
Current CPC
Class: |
A01B 79/005 20130101;
G01D 18/00 20130101 |
Class at
Publication: |
703/2 ;
702/85 |
International
Class: |
G06F 17/10 20060101
G06F017/10; G06F 19/00 20110101 G06F019/00 |
Claims
1. A method for calibrating agricultural measurements comprising:
obtaining aerial data representing relative measurements of an
agricultural metric in a geographic area, the relative measurements
having an unknown bias; obtaining ground-based data representing
absolute measurements of the agricultural metric within the
geographic area; and, using the absolute measurements to calibrate
the relative measurements, thereby synthesizing absolute
measurements of the agricultural metric in parts of the geographic
area.
2. The method of claim 1, the aerial data obtained from a
satellite.
3. The method of claim 1, the aerial data obtained from an
airplane.
4. The method of claim 1, the agricultural metric being normalized
difference vegetative index.
5. The method of claim 1, the agricultural metric being a
reflectance-based vegetative index.
6. The method of claim 1 further comprising: combining data
representing the ground-based and synthesized absolute measurements
with additional spatial agricultural data to generate a
prescription for the application of chemicals to an agricultural
field.
7. The method of claim 6, the additional spatial agricultural data
being a soil data map.
8. The method of claim 6, the additional spatial agricultural data
being a crop data map.
9. The method of claim 6, the additional spatial agricultural data
being climate data.
10. The method of claim 6, the chemicals being fertilizers.
11. The method of claim 6, the chemicals being pesticides or
herbicides.
12. The method of claim 6, the prescription based on an
agricultural algorithm having an agricultural metric and climate
data as inputs.
13. The method of claim 12, the agricultural metric being
normalized difference vegetative index and the climate data
including growing degree days since planting.
14. The method of claim 1, the synthesizing absolute measurements
including using a plant growth model to propagate ground-based data
forward or backward in time as needed to compare it with
non-contemporaneous satellite data.
15. The method of claim 14, the plant growth model being a linear
model.
16. A system for making calibrate agricultural measurements
comprising: a source of aerial data representing relative
measurements of an agricultural metric in a geographic area, the
relative measurements having an unknown bias; a source of
ground-based data representing absolute measurements of the
agricultural metric within the geographic area; and, a database and
processor that use the absolute measurements to calibrate the
relative measurements, thereby synthesizing absolute measurements
of the agricultural metric in parts of the geographic area.
17. The system of claim 16, the aerial data obtained from a
satellite.
18. The system of claim 16, the aerial data obtained from an
airplane.
19. The system of claim 16, the agricultural metric being
normalized difference vegetative index.
20. The system of claim 16, the agricultural metric being a
reflectance-based vegetative index.
21. The system of claim 16, the database and processor further
combining data representing the ground-based and synthesized
absolute measurements with additional spatial agricultural data to
generate a prescription for the application of chemicals to an
agricultural field.
22. The system of claim 16, the additional spatial agricultural
data being a soil data map.
23. The system of claim 15, the additional spatial agricultural
data being a crop data map.
24. The system of claim 16, the additional spatial agricultural
data being climate data.
25. The system of claim 16, the chemicals being fertilizers.
26. The system of claim 16, the chemicals being pesticides or
herbicides.
27. The system of claim 16, the prescription based on an
agricultural algorithm having an agricultural metric and climate
data as inputs.
28. The system of claim 27, the agricultural metric being
normalized difference vegetative index and the climate data
including growing degree days since planting.
29. The system of claim 16, the synthesizing absolute measurements
including using a plant growth model to propagate ground-based data
forward or backward in time as needed to compare it with
non-contemporaneous satellite data.
30. The system of claim 16, the plant growth model being a linear
model.
Description
TECHNICAL FIELD
[0001] The disclosure is related to monitoring and prediction of
agricultural performance over wide areas.
BACKGROUND
[0002] A modern crop farm may be thought of as a complex
biochemical factory optimized to produce corn, wheat, soybeans or
countless other products, as efficiently as possible. The days of
planting in spring and waiting until fall harvest to assess results
are long gone. Instead, today's best farmers try to use all
available data to monitor and promote plant growth throughout a
growing season. Farmers influence their crops through the
application of fertilizers, growth regulators, harvest aids,
herbicides and pesticides. Precise crop monitoring--to help decide
quantity, location and timing of field applications--has a profound
effect on cost, crop yield and pollution.
[0003] Normalized difference vegetative index (NDVI) is an example
of a popular crop metric. NDVI is based on differences in optical
reflectivity of plants and dirt at different wavelengths. Dirt
reflects more visible (VIS) red light than near-infrared (NIR)
light, while plants reflect more NIR than VIS. Chlorophyll in
plants is a strong absorber of visible red light; hence,
plants'characteristic green color.
NDVI = r NIR - r VIS r NIR + r VIS , ##EQU00001##
where r is reflectivity measured at the wavelength indicated by the
subscript. Typically, NIR is around 770 nm while VIS is around 660
nm. In various agricultural applications, NDVI correlates well with
biomass, plant height, nitrogen content or frost damage.
[0004] Farmers use NDVI measurements to decide when and how much
fertilizer to apply. Early in a growing season it may be hard to
gauge how much fertilizer plants will need over the course of their
growth. Too late in the season, the opportunity to supply missing
nutrients may be lost. Thus the more measurements are available
during a season, the better.
[0005] A crop's yield potential is the best yield obtainable for a
particular plant type in a particular field and climate. Farmers
often apply a high dose of fertilizer, e.g. nitrogen, to a small
part of a field, the so-called "N-rich strip". This area has enough
nitrogen to ensure that nitrogen deficiency does not retard plant
growth. NDVI measurements on plants in other parts of the field are
compared with those from the N-rich strip to see if more nitrogen
is needed to help the field keep up with the strip.
[0006] The consequences of applying either too much or too little
nitrogen to a field can be severe. With too little nitrogen the
crop may not achieve its potential and profit may be left "on the
table." Too much nitrogen, on the other hand, wastes money and may
cause unnecessary pollution during rain runoff. Given imperfect
information, farmers tend to over apply fertilizer to avoid the
risk of an underperforming crop. Thus, more precise and accurate
plant growth measurements save farmers money and prevent pollution
by reducing the need for over application.
[0007] NDVI measurements may be obtained from various sensor
platforms, each with inherent strengths and weaknesses. Satellite
or aerial imaging can quickly generate NDVI maps that cover wide
areas. However, satellites depend on the sun to illuminate their
subjects and the sun is rarely, if ever, directly overhead a field
when a satellite acquires an image. Satellite imagery is also
affected by atmospheric phenomena such as clouds and haze. These
effects lead to an unknown bias or offset in NDVI readings obtained
by satellites or airplanes. Relative measurements within an image
are useful, but comparisons between images, especially those taken
under different conditions or at different times, may not be
meaningful.
[0008] Local NDVI measurements may be obtained with ground based
systems such as the Trimble Navigation "GreenSeeker". A GreenSeeker
is an active sensor system that has its own light source that is
scanned approximately one meter away from plant canopy. The light
source is modulated to eliminate interference from ambient light.
Visible and near-infrared reflectivity are measured from
illumination that is scanned over a field. Ground-based sensors
like the GreenSeeker can be mounted on tractors, spray booms or
center-pivot irrigation booms to scan an entire field.
(GreenSeekers and other ground-based sensors may also be hand-held
and, optionally, used with portable positioning and data collection
devices such as laptop computers, portable digital assistants,
smart phones or dedicated data controllers.) Active, ground-based
sensors provide absolute measurements that may be compared with
other measurements obtained at different times, day or night. It
does take time, however, to scan the sensors over fields of
interest.
[0009] What is needed are wide area plant monitoring systems and
methods capable of providing absolute data that can be compared
with other data obtained by different methods and/or at different
times. Furthermore, farmers need help navigating the vast stores of
potentially valuable data that affect plant growth.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 shows a schematic map of nine farm fields with
management zones.
[0011] FIG. 2 shows one of the fields of FIG. 1 in greater
detail.
[0012] FIG. 3 shows a schematic satellite image of the fields of
FIG. 1.
[0013] FIG. 4 shows a block diagram of a wide-area field
prescription system.
[0014] FIG. 5 shows a block diagram of a method to combine
satellite and ground data acquired at different times.
[0015] FIGS. 6A and 6B show a schematic graph of NDVI data obtained
at different times via different methods.
DETAILED DESCRIPTION
[0016] Wide-area agricultural monitoring and prediction encompasses
systems and methods to generate calibrated estimates of plant
growth and corresponding field prescriptions. Data from ground and
satellite based sensors are combined to obtain absolute, calibrated
plant metrics, such as NDVI, over wide areas. Further inputs, such
as soil, crop characteristics and climate data, are stored in a
database. A processor uses the measured plant metrics and database
information to create customized field prescription maps that show
where, when and how much fertilizer, pesticide or other treatment
should be applied to a field to maximize crop yield.
[0017] Ground data are used to remove the unknown bias or offset of
satellite or aerial images thereby allowing images taken at
different times to be compared with each other or calibrated to an
absolute value. Soil, crop and climate data may also be stored as
images or maps. The volume of data stored in the database can be
quite large depending on the area of land covered and the spatial
resolution. Simulations of plant growth may be run with plant and
climate models to build scenarios such that a farmer can predict
not just what may happen to his crops based on average assumptions,
but also probabilities for outlying events.
[0018] A basic ingredient of any field prescription, however, is an
accurate map of actual plant progress measured in the field. NDVI
is used here as a preferred example of a metric for measuring plant
growth; however, other parameters, such as the green vegetation
index, or other reflectance-based vegetative indices, may also be
useful. FIG. 1 shows a schematic map of nine farm fields, 101, 102
. . . 109, delineated by solid boundary lines. Dashed lines in the
figure show the boundaries of field management zones which are
labeled by circled numbers 1, 2 and 3. Management zones are areas
of common growing characteristics. Qualities that define a zone may
include drainage, soil type, ground slope, naturally occurring
nutrients, weed types, pests, etc. Regardless of how zones differ,
plants within a zone tend to grow about the same. Targeted
fertilizer application within a zone can help smooth out growth
variation. Plants in different zones may require markedly different
fertilizer prescriptions.
[0019] FIG. 2 shows field 107 of FIG. 1 in greater detail. The
field overlaps three management zones labeled by circled numbers 1,
2 and 3. Path 205 shows the track that a ground-based NDVI scanner
like a GreenSeeker takes as it measures plant growth in the field.
Ground-based scanners can be deployed on tractors, spray trucks or
other equipment and can be programmed to record data whenever the
equipment moves over a growing area. (Ground-based scanners may
also be hand-held and connected to portable data collection and/or
positioning equipment.) Ground-based scanners are often used for
real-time, variable-rate application, but because the scanners are
automated, they can run any time, not just during fertilizer
application.
[0020] In FIG. 2, gray stripe 210 marks the location of an N-rich
strip. The N-rich strip is an area where an excess of nitrogen
fertilizer has been applied. Plant growth in the N-rich strip is
not limited by the availability of nitrogen, so those plants
exhibit the maximum yield potential of similar plants in the field:
Because N-rich strips are useful for yield potential calculations,
measurement of NDVI in an N-rich strip is often part of a
real-time, variable-rate application procedure. N-rich strips are
not always needed, however. The performance of the top 10% of
plants in a representative part of a field may provide an adequate
standard for maximum yield potential, for example.
[0021] FIG. 3 shows a schematic satellite image of the fields of
FIG. 1. The area of land illustrated in FIG. 3 is the same as the
area shown in FIG. 1. The land in FIG. 3 has been divided into
pixels (e.g. 301, 302, 303, 304) similar to those that may be
obtained by satellite imaging. FIG. 3 is drawn for purposes of
illustration only; it is not to scale. Pixels in an actual
satellite image may represent areas in the range of roughly 1
m.sup.2 to roughly 100 m.sup.2. The resolution of today's satellite
images is suitable for agricultural purposes; it is no longer a
limiting factor as was the case several years ago.
[0022] Scale 305 in FIG. 3 is a schematic representation of an NDVI
scale. Darker pixels represent higher values of NDVI. Although only
five relative NDVI levels are shown in FIG. 3, much higher
precision is available from actual satellite images. Actual
satellite images, however, do not provide absolute NDVI with the
high accuracy available using ground-based sensors. Variations in
lighting (i.e. position of the sun), atmospheric effects (e.g.
clouds, haze, dust, rain, etc.), and satellite position all
introduce biases and offsets that are difficult to quantify.
[0023] It is apparent that NDVI measurements for the set of fields
shown in FIGS. 1 and 3 may be obtained by either ground or
satellite sensors. Ground measurements provide absolute NDVI at
high accuracy while satellite measurements provide relative NDVI
over wide areas. When ground and satellite data are available for a
common area at times that are not too far apart, the ground data
may be used to resolve the unknown bias or offset in the satellite
data. As an example, if field 107 in FIG. 1 is measured by a
GreenSeeker scan and fields 101 through 109 (including 107) are
measured by satellite imaging, then overlapping ground and
satellite data for field 107 can be used to calibrate the satellite
data for all of the fields. The accuracy of ground-based data has
been extended to a wide area. Generally "times that are not too far
apart" are within a few days of one another; however, the actual
maximum time difference for useful calibration depends on how fast
plants are growing. Measurements must be closer together in time
for fast-growing crops. Methods to estimate plant growth rate and
extend the amount by which ground and satellite measurements can be
separated in time are discussed below.
[0024] FIG. 4 shows a block diagram of a wide-area field
prescription system. In FIG. 4, ground data 405 and satellite data
410 are inputs to a database and processor 430. The output from the
database and processor is a field prescription 435; i.e. a plan
detailing how much chemical application is needed to optimize yield
from a farm field. A field prescription may be visualized as a map
showing when, where and how much fertilizer or pesticide is
required on a field. The prescription may be used by an automated
application system such as a spray truck with dynamically
controllable spray nozzles.
[0025] Soil data 415, crop data 420 and climate data 425 may also
be inputs to the database and processor although not all of these
data may be needed for every application. All of the data sources
405 through 425, and other data not shown, are georeferenced. Each
data point (soil type, crop type, climate history, NDVI from
various sources, etc.) is associated with a location specified in
latitude and longitude or any other convenient mapping coordinate
system. The various data may be supplied at different spatial
resolution. Climate data, for example, is likely to have lower
spatial resolution than soil type.
[0026] Data inputs 405 through 425 are familiar to agronomists as
inputs to plant yield potential algorithms. Database and processor
430 are thus capable of generating wide-area field prescriptions
based on any of several possible plant models and algorithms. The
ability to run different hypothetical scenarios offers farmers a
powerful tool to assess the risks and rewards of various fertilizer
or pesticide application strategies. For example, a farmer might
simulate the progress of one of his fields given rainfall and
growing degree day scenarios representing average growing
conditions and also growing conditions likely to occur only once
every ten years. Furthermore, the farmer may send a ground-based
NDVI sensor to scan small parts of just a few of his fields
frequently, perhaps once a week, for example. These small data
collection areas may then be used to calibrate satellite data
covering a large farm. The resulting calibrated data provides the
farmer with more precise estimates of future chemical needs and
reduces crop yield uncertainty.
[0027] It is rarely possible to obtain ground and satellite NDVI
data measured at the same time. If only a few days separate the
measurements, the resulting errors may be small enough to ignore.
However, better results may be obtained by using a plant growth
model to propagate data forward or backward in time as needed to
compare asynchronous sources. FIG. 5 shows a block diagram of a
method to combine satellite and ground data acquired at different
times.
[0028] In FIG. 5, ground data 505, e.g. NDVI obtained by a
GreenSeeker, and satellite data 510 are inputs to a plant growth
model 515. Results from the model are used to generate an NDVI map
520 for any desired time. Most plants' growth is described
approximately by a sigmoid function; the part of the sigmoid of
interest to farmers is the main growth phase which is approximately
exponential. Furthermore, for data not separated too far in time,
plants' exponential growth may be approximated by a linear growth
model.
[0029] The use of a linear plant growth model to compare
asynchronous ground-based and satellite measurements of NDVI may be
understood by referring to FIGS. 6A and 6B that show a schematic
graph of NDVI data obtained at different times via different
methods. In FIG. 6A NDVI is plotted versus time for a small area,
for example a single data point in a farm field, or a small section
of a field. NDVI measurements 605 and 610 are obtained by a
ground-based system at times t.sub.1 and t.sub.2 respectively,
while NDVI measurement 614 is obtained from a satellite image at a
later time t.sub.3. Satellite-derived data point 614 has a bias or
offset. The bias in data point 614 may be calculated by fitting
line 620 to ground-derived data points 605 and 610. The result is
that the actual NDVI measured by the satellite at time t.sub.3 (for
the specific ground area under consideration in FIG. 6A) is
represented by data point 616, the value of the function
represented by line 620 at t.sub.3. Of course, the longer the
interval between t.sub.2 and t.sub.3, the less confidence may be
placed in linear extrapolation 620. However, the result is likely
more accurate than simply forcing data point 614 to have the same
value as data point 610, for example.
[0030] The situation plotted in FIG. 6B is similar to that of FIG.
6A except for the order in which data is obtained. In FIG. 6B NDVI
measurements 625 and 635 are obtained by a ground-based system at
times t.sub.4 and t.sub.6 respectively, while NDVI measurement 628
is obtained from a satellite image at an intermediate time t.sub.5.
Satellite-derived data point 628 has a bias or offset. The bias in
data point 628 may be calculated by fitting line 640 to
ground-derived data points 625 and 635. The result is that the
actual NDVI measured by the satellite at time t.sub.5 (for the
specific ground area under consideration in FIG. 6B) is represented
by data point 632, the value of the function represented by line
640 at t.sub.5.
[0031] FIGS. 6A and 6B have been described in a simplified scenario
in which plant growth is assumed to be easily modeled as a function
of time. However, it may be more realistic to express plant growth
as a function of heat input, represented for example by growing
degree days since planting. If the number of growing degree days
per actual day does not change (an idealized and somewhat unlikely
scenario), then plant growth versus time or heat input will have
the same functional form. In general, the time axis in FIGS. 6A and
6B may be replaced by a model which may include heat input,
moisture, rainfall, sunlight intensity or other data that affect
growth rate.
[0032] It will be apparent to those skilled in the art that the
methods discussed above in connection with FIGS. 5 and 6 may be
generalized. Two measurement sources--ground and satellite
sensors--measure the same quantity. One source provides absolute
measurements while the other includes an unknown bias. A linear
model may be used for the time evolution of the measured quantity,
NDVI. The situation is well suited for the application of a digital
filter, such as a Kalman filter, to obtain an optimal estimate for
NDVI. Relative measurements of NDVI over wide areas are calibrated
by absolute measurements over smaller, subset areas.
[0033] Sparse spatial NDVI sampling may be sufficient to calibrate
wide-area satellite data. More dense sampling is needed for smaller
management zones which are often associated with more rapidly
varying topography, while less dense sampling is sufficient for
larger management zones which are often associated with flatter
topography.
[0034] The wide-area agricultural and prediction systems and
methods described herein give farmers more precise and accurate
crop information over wider areas than previously possible. This
information may be combined with soil, climate, crop and other
spatial data to generate field prescriptions using standard or
customized algorithms.
[0035] Although many of the systems and methods have been described
in terms of fertilizer application, the same principles apply to
pesticide, herbicide and growth regulator application as well.
Although many of the systems and methods have been described as
using images obtained from satellites, the same principles apply to
images obtained from airplanes, helicopters, balloons, unmanned
aerial vehicles (UAVs) and other aerial platforms. Thus "aerial
data" comprises data obtained from satellite, airplane, helicopter,
balloon and UAV imaging platforms. Similarly, "ground-based data"
comprises data obtained from sensors that may be mounted on a
truck, tractor or other vehicle, or that may be hand-held. Although
many of the systems and methods have been described in terms of
NDVI, other reflectance-based vegetative indices may be used.
[0036] The above description of the disclosed embodiments is
provided to enable any person skilled in the art to make or use the
disclosure. Various modifications to these embodiments will be
readily apparent to those skilled in the art, and the principles
defined herein may be applied to other embodiments without
departing from the scope of the disclosure. Thus, the disclosure is
not intended to be limited to the embodiments shown herein but is
to be accorded the widest scope consistent with the principles and
novel features disclosed herein.
* * * * *