METHOD AND SYSTEM TO PRESCRIBE VARIABLE SEEDING DENSITY ACROSS A CULTIVATED FIELD USING REMOTELY SENSED DATA

• US 20150206255A1
• Filed: 04/01/2015
• Published: 07/23/2015
• Est. Priority Date: 05/13/2011
• Status: Abandoned Application

• Current Assignee: The Climate Corporation (Bayer AG)
• Original Assignee: HydroBio, Inc. (Bayer AG)
• Inventors: Groeneveld, David P.
• Application Number:
  US14/676,660
• Publication Number:
  US 20150206255A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A01B 79/005 Precision agriculture
• CPC Class Codes: A01C 21/00 Methods of fertilising , sowing or planting
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 50/02 Agriculture; Fishing; Forestry; Mining

Abstract

A method for prescribing variable seed density planting. The method can include: obtaining first EOS data collected approximately on an estimated day (DOY′) during a past crop-growing season in which NDVI* data most closely resembles a spatial-yield pattern measured during harvest in the past crop-growing season; converting the first EOS data to first reflectance data and first NDVI data; calculating first NDVI* data on a per pixel basis for the first EOS data based on the first NDVI data using satellite scene statistics of the first EOS data; generating an NDVI* map for a first field using the first NDVI* data for the first EOS data; and generating a variable seed density prescription map using the NDVI* map. The variable seed density prescription map can be spatially defined. Other embodiments are provided.

Claims

1. A method for prescribing variable seed density planting, the method comprising:

obtaining first EOS data collected approximately on an estimated day (DOY′

) during a past crop-growing season in which NDVI* data most closely resembles a spatial-yield pattern measured during harvest in the past crop-growing season;

converting the first EOS data to first reflectance data and first NDVI data;

calculating first NDVI* data on a per pixel basis for the first EOS data based on the first NDVI data using satellite scene statistics of the first EOS data;

generating an NDVI* map for a first field using the first NDVI* data for the first EOS data; and

generating a variable seed density prescription map using the NDVI* map, the variable seed density prescription map being spatially defined.

2. The method of claim 1, further comprising:

determining when the DOY′

will occur for the first field growing a first crop type within a first farming region, comprising;

obtaining second EOS data collected through the past crop-growing season for the first farming region, the first farming region comprising an area having approximately a same climate and day length as the first field;

converting the second EOS data to second reflectance data and second NDVI data;

calculating second NDVI* data from the second NDVI data using satellite scene statistics of the second EOS data;

extracting the second NDVI* data for the first crop type on the first field;

determining an apparent emergence date (AED) for the first crop on the first field;

mapping the second NDVI* data across the first field for a latter at least one-third of the past crop-growing season on NDVI* maps;

displaying spatial yield data recorded spatially during harvest for the first field on a spatial yield data map for comparison with the NDVI* maps; and

receiving a selection for the DOY′

based on one of the NDVI* maps that best corresponds to the spatial yield data map.

3. The method of claim 2, wherein:

determining when the DOY′

will occur for the first field growing the first crop type within the first farming region further comprises;

calculating elapsed days from the AED to the DOY′

for the first crop type on the first field;

collecting a set of samples of elapsed day values based on AED values for a plurality of fields growing the first crop type within the first farming region, the plurality of fields comprising the first field;

graphing the set of samples of elapsed day values against the AED values for each of the plurality of fields; and

determining an estimated number of elapsed days from the AED to the DOY′

for a future field growing the first crop type within the first farming region.

4. The method of claim 3, wherein:

determining an estimated number of elapsed days from the AED to the DOY′

for a future field growing the first crop type within the first farming region comprises using linear regression.

5. The method of claim 2, wherein:

determining the apparent emergence date (AED) for the first crop on the field comprises;

graphing median values of the second NDVI* data for the first crop type on the first field by day of year (DOY);

selecting a first set of the median values of the second NDVI* data during a linear growth phase of the first crop type on the first field;

performing linear regression on the first set of the median values of the second NDVI* data in the linear growth phase of the first crop type on the first field; and

solving a linear equation resulting from the linear regression to yield the AED for the first crop type on the first field.

6. The method of claim 1, further comprising:

estimating when the DOY′

occurred for the first field growing a first crop type within a first farming region, comprising;

obtaining multiple sets of EOS data collected during a linear growth phase of the first crop type grown in the farming region an immediately prior crop-growing season, the first farming region comprising an area having approximately a same climate and day length as the first field;

converting the multiple sets of EOS data to second reflectance data, second NDVI data, and second NDVI* data;

determining an apparent emergence date (AED) for the first crop on the first field using linear regression on the second NDVI* data as expressed by day of year (DOY);

predicting the DOY′

using the AED for the first field;

selecting an archived image for a date that most closely corresponds to the DOY′

, for the first field growing the first crop type within the first farming region;

extracting NDVI* pixel values from a portion of the multiple set of EOS data having a date that most closely corresponds to the calculated DOY′

for the first field with the first crop type; and

assembling a digital map of the NDVI* pixel values across the first field for the first crop type.

7. The method of claim 1, wherein:

generating a variable seed density prescription map using the NDVI* map further comprises;

obtaining a maximum recommended seeding density for the first crop type;

determining a variable seeding density based on the first NDVI* data across the NDVI* map for the first field, wherein the first NDVI* data is scaled based on (a) the maximum recommended seeding density for the first crop type being equivalent to an NDVI* value of 1.0 and (b) a minimum seeding density of zero being equivalent to an NDVI* value of zero, and wherein seeding densities for intermediate values are interpolated based on the scaling of the first NDVI* data and the NDVI* map; and

generating the variable seed density prescription map based on the variable seeding density as spatially defined across the first field.

8. The method of claim 1, wherein:

generating the variable seed density prescription map using the NDVI* map further comprises;

using a maximum seeding density for a first crop type on the first field based on an experience of a farmer of the first field;

setting the maximum seeding density for the first field and the first crop type equivalent to a maximum NDVI* value on the NDVI* map and a zero seeding density equivalent to a zero NDVI* value;

determining a variable seeding density by interpolating the first NDVI* data on the NDVI* map between the maximum seeding density and the zero seeding density; and

generating the variable seed density prescription map based on the variable seeding density as spatially defined across the first field.

9. The method of claim 1, further comprising:

planting spatially variable densities of seeds across the first field according to the variable seed density prescription map.

10. The method of claim 9, wherein:

planting spatially variable densities of the seeds across the first field according to the variable seed density prescription map further comprises;

identifying farm planting equipment that is equipped with a variable-seeding-density controller and a GPS location device; and

transferring the variable seed density prescription map to the farm planting equipment through an API of the farm planting equipment for the farm planting equipment to plant densities of the seeds across the first field according to position information provided by the GPS location device of the farm planning equipment and by seed density information provided by the variable seed density prescription map.

11. A system for prescribing variable seed density planting, the system comprising:

one or more processing modules; and

one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform the acts of;

obtaining first EOS data collected approximately on an estimated day (DOY′

) during a past crop-growing season in which NDVI* data most closely resembles a spatial-yield pattern measured during harvest in the past crop-growing season;

converting the first EOS data to first reflectance data and first NDVI data;

calculating first NDVI* data on a per pixel basis for the first EOS data based on the first NDVI data using satellite scene statistics of the first EOS data;

generating an NDVI* map for a first field using the first NDVI* data for the first EOS data; and

generating a variable seed density prescription map using the NDVI* map, the variable seed density prescription map being spatially defined.

12. The system of claim 11, wherein the computing instructions are further configured to perform the acts of:

determining when the DOY′

will occur for the first field growing a first crop type within a first farming region, comprising;

obtaining second EOS data collected through the past crop-growing season for the first farming region, the first farming region comprising an area having approximately a same climate and day length as the first field;

converting the second EOS data to second reflectance data and second NDVI data;

calculating second NDVI* data from the second NDVI data using satellite scene statistics of the second EOS data;

extracting the second NDVI* data for the first crop type on the first field;

determining an apparent emergence date (AED) for the first crop on the first field;

mapping the second NDVI* data across the first field for a latter at least one-third of the past crop-growing season on NDVI* maps;

displaying spatial yield data recorded spatially during harvest for the first field on a spatial yield data map for comparison with the NDVI* maps; and

receiving a selection for the DOY′

based on one of the NDVI* maps that best corresponds to the spatial yield data map.

13. The system of claim 12, wherein:

determining when the DOY′

will occur for the first field growing the first crop type within the first farming region further comprises;

calculating elapsed days from the AED to the DOY′

for the first crop type on the first field;

collecting a set of samples of elapsed day values based on AED values for a plurality of fields growing the first crop type within the first farming region, the plurality of fields comprising the first field;

graphing the set of samples of elapsed day values against the AED values for each of the plurality of fields; and

determining an estimated number of elapsed days from the AED to the DOY′

for a future field growing the first crop type within the first farming region.

14. The system of claim 13, wherein:

determining an estimated number of elapsed days from the AED to the DOY′

for a future field growing the first crop type within the first farming region comprises using linear regression.

15. The system of claim 12, wherein:

determining the apparent emergence date (AED) for the first crop on the field comprises;

graphing median values of the second NDVI* data for the first crop type on the first field by day of year (DOY);

selecting a first set of the median values of the second NDVI* data during a linear growth phase of the first crop type on the first field;

performing linear regression on the first set of the median values of the second NDVI* data in the linear growth phase of the first crop type on the first field; and

solving a linear equation resulting from the linear regression to yield the AED for the first crop type on the first field.

16. The system of claim 11, wherein the computing instructions are further configured to perform the acts of:

estimating when the DOY′

occurred for the first field growing a first crop type within a first farming region, comprising;

obtaining multiple sets of EOS data collected during a linear growth phase of the first crop type grown in the farming region an immediately prior crop-growing season, the first farming region comprising an area having approximately a same climate and day length as the first field;

converting the multiple sets of EOS data to second reflectance data, second NDVI data, and second NDVI* data;

determining an apparent emergence date (AED) for the first crop on the first field using linear regression on the second NDVI* data as expressed by day of year (DOY);

predicting the DOY′

using the AED for the first field;

selecting an archived image for a date that most closely corresponds to the DOY′

, for the first field growing the first crop type within the first farming region;

extracting NDVI* pixel values from a portion of the multiple set of EOS data having a date that most closely corresponds to the calculated DOY′

for the first field with the first crop type; and

assembling a digital map of the NDVI* pixel values across the first field for the first crop type.

17. The system of claim 11, wherein:

generating a variable seed density prescription map using the NDVI* map further comprises;

obtaining a maximum recommended seeding density for the first crop type;

determining a variable seeding density based on the first NDVI* data across the NDVI* map for the first field, wherein the first NDVI* data is scaled based on (a) the maximum recommended seeding density for the first crop type being equivalent to an NDVI* value of 1.0 and (b) a minimum seeding density of zero being equivalent to an NDVI* value of zero, and wherein seeding densities for intermediate values are interpolated based on the scaling of the first NDVI* data and the NDVI* map; and

generating the variable seed density prescription map based on the variable seeding density as spatially defined across the first field.

18. The system of claim 11, wherein:

generating the variable seed density prescription map using the NDVI* map further comprises;

using a maximum seeding density for a first crop type on the first field based on an experience of a farmer of the first field;

setting the maximum seeding density for the first field and the first crop type equivalent to a maximum NDVI* value on the NDVI* map and a zero seeding density equivalent to a zero NDVI* value;

determining a variable seeding density by interpolating the first NDVI* data on the NDVI* map between the maximum seeding density and the zero seeding density; and

generating the variable seed density prescription map based on the variable seeding density as spatially defined across the first field.

19. The system of claim 11, wherein the computing instructions are further configured to perform the acts of:

planting spatially variable densities of seeds across the first field according to the variable seed density prescription map.

20. The system of claim 19, wherein:

planting spatially variable densities of the seeds across the first field according to the variable seed density prescription map further comprises;

identifying farm planting equipment that is equipped with a variable-seeding-density controller and a GPS location device; and

transferring the variable seed density prescription map to the farm planting equipment through an API of the farm planting equipment for the farm planting equipment to plant densities of the seeds across the first field according to position information provided by the GPS location device of the farm planning equipment and by seed density information provided by the variable seed density prescription map.