IP Library Granted Patent US 12,165,222
Granted Patent B2
US 12,165,222 · App. 17/681,126 · Granted Dec 10, 2024

Imagery-based boundary identification for agricultural fields

Inventors: Bobby Harold Braswell (Portsmouth, NH); Tina A. Cormier (Brunswick, ME); Damien Sulla-Menashe (Cambridge, MA); Keith Frederick Ma (Cambridge, MA)
Assignee: INDIGO AG, INC.
G06Q50/02G06T5/70G06T7/11G06T7/13G06T7/162G06V10/26G06V10/30G06V10/32G06V10/34G06V10/60G06V20/188G06T2207/10032
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Quick Facts
Patent No.
US 12,165,222
App. No.
17/681,126
Granted
Dec 10, 2024
Kind
B2
Abstract

Imagery-based boundary identification for agricultural fields is provided. In various embodiments, a time series of surface reflectance rasters for a geographic region is received. For each of the surface reflectance rasters, at least one index raster is determined, yielding at least one time series of index rasters. The at least one time series of index rasters is divided into a plurality of consecutive time windows. The at least one time series of index rasters is composited within each of the plurality of time windows, yielding a composite index raster for each of the at least one time series of index rasters in each of the plurality of time windows. The composite index rasters are segmented into a plurality of spatially compact regions of the geographic region. A plurality of polygons is generated from the plurality of spatially compact regions, each of the plurality of polygons corresponding to an agricultural field in the geographic region.

Claims (38)

1. A method comprising:

receiving a time series of surface reflectance rasters for a geographic region;

determining, for each of the surface reflectance rasters, at least one index raster to produce one or more time series of index rasters;

dividing one or more time series of index rasters into a plurality of consecutive time windows;

compositing one or more time series of index rasters within each of the plurality of time windows, to produce a composite index raster for each of one or more time series of index rasters in each of the plurality of time windows;

segmenting the composite index rasters into a plurality of spatially compact regions of the geographic region by denoising the composite index raster using a spatial low pass filter; and

generating a plurality of polygons from the plurality of spatially compact regions, each of the plurality of polygons corresponding to an agricultural field in the geographic region.

2. The method of claim 1 , wherein the time series of surface reflectance rasters comprises satellite data.

3. The method of claim 1 , wherein the time series of surface reflectance rasters spans a growing season in the geographic region.

4. The method of claim 1 , wherein receiving the time series of surface reflectance rasters comprises determining surface reflectance from uncorrected reflectance data.

5. The method of claim 1 , wherein the at least one index raster comprises a normalized difference vegetation index raster.

6. The method of claim 1 , wherein the at least one index raster comprises a land surface water index raster.

7. The method of claim 1 , wherein the at least one index raster comprises a mean brightness raster.

8. The method of claim 1 , wherein determining the at least one index raster comprises downsampling the surface reflectance rasters.

9. The method of claim 1 , wherein the plurality of consecutive time windows correspond to early, mid-, and late phases of a growing season in the geographic region.

10. The method of claim 1 , wherein compositing comprises averaging the at least one time series of index rasters within each of the plurality of time windows.

11. The method of claim 1 , wherein segmenting comprises filling missing pixels in the composite index rasters.

12. The method of claim 11 , wherein filling missing pixels comprises applying linear interpolation to the composite index rasters.

13. The method of claim 1 , wherein segmenting comprises normalizing the composite index rasters.

14. The method of claim 1 , wherein segmenting comprises graph-based segmentation.

15. The method of claim 1 , wherein segmenting comprises Felzenszwalb segmentation.

16. The method of claim 1 , wherein generating the plurality of polygons comprises applying spatial smoothing to the plurality of spatially compact regions.

17. A system comprising:

a datastore comprising a time series of surface reflectance rasters for a geographic region; and

a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising:

receiving the time series of surface reflectance rasters for a geographic region;

determining, for each of the surface reflectance rasters, at least one index raster to produce one or more time series of index rasters;

dividing one or more time series of index rasters into a plurality of consecutive time windows;

compositing one or more time series of index rasters within each of the plurality of time windows, to produce a composite index raster for each of one or more time series of index rasters in each of the plurality of time windows;

segmenting the composite index rasters into a plurality of spatially compact regions of the geographic region by denoising the composite index raster using a spatial low pass filter; and

generating a plurality of polygons from the plurality of spatially compact regions, each of the plurality of polygons corresponding to an agricultural field in the geographic region.

18. A computer program product for agricultural field boundary identification, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

receiving the time series of surface reflectance rasters for a geographic region;

determining, for each of the surface reflectance rasters, at least one index raster to produce one or more time series of index rasters;

dividing one or more time series of index rasters into a plurality of consecutive time windows;

compositing one or more time series of index rasters within each of the plurality of time windows, to produce a composite index raster for each of one or more time series of index rasters in each of the plurality of time windows;

segmenting the composite index rasters into a plurality of spatially compact regions of the geographic region by denoising the composite index raster using a spatial low pass filter; and

generating a plurality of polygons from the plurality of spatially compact regions, each of the plurality of polygons corresponding to an agricultural field in the geographic region.

Assignments (5)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 15, 2026
From: INDIGO AG, LLC
To: INDIGO AGRICULTURE, INC.
Reel/Frame 075279/0881 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 15, 2026
From: INDIGO AGRICULTURE, INC.
To: TERION AI, INC.
Reel/Frame 075280/0039 →
RELEASE OF SECURITY INTEREST Recorded Oct 25, 2023
From: CORTLAND CAPITAL MARKET SERVICES LLC
To: INDIGO AG, INC.; INDIGO AGRICULTURE, INC.
Reel/Frame 065344/0780 →
SECURITY INTEREST Recorded Aug 10, 2023
From: INDIGO AGRICULTURE, INC.; INDIGO AG, INC.
To: CORTLAND CAPITAL MARKET SERVICES LLC, AS AGENT
Reel/Frame 064559/0438 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 25, 2023
From: BRASWELL, BOBBY HAROLD; CORMIER, TINA A.; SULLA-MENASHE, DAMIEN; MA, KEITH FREDERICK
To: INDIGO AG, INC.
Reel/Frame 063767/0755 →
Continuity (3)
Continuation PCTUS2020048188 · Aug 27, 2020
Provisional Application 62892110 · Aug 27, 2019
Related Publication 20220180526A1 · Jun 9, 2022