Wide-area agricultural monitoring and prediction

• US 8,731,836 B2
• Filed: 12/17/2013
• Issued: 05/20/2014
• Est. Priority Date: 10/25/2010
• Status: Active Grant

• Current Assignee: Trimble Navigation Limited (Trimble Inc.)
• Original Assignee: Trimble Navigation Limited (Trimble Inc.)
• Inventors: Ulman, Morrison, Lindores, Robert J, Mayfield, Ted E
• Primary Examiner(s): Kundu, Sujoy
• Assistant Examiner(s): Ngon, Ricky
• Application Number:
  US14/109,003
• Publication Number:
  US 20140107957A1
• Time in Patent Office: 154 Days
• Field of Search: 702/2, 702/3, 702/5, 702/188, 382/110, 382/195
• US Class Current: 702/2
• CPC Class Codes: A01B 79/005 Precision agriculture
• CPC Class Codes: G01D 18/00 Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00

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.

Claims

1. A method for calibrating agricultural measurements comprising:

using an aerial sensor to obtain aerial data representing relative measurements of an agricultural metric in a geographic area, the relative measurements having an unknown bias;

using a ground-based sensor to obtain ground-based data representing absolute measurements of the agricultural metric within the geographic area; and

,

a processor using the absolute measurements to calibrate the relative measurements, thereby synthesizing absolute measurements of the agricultural metric in parts of the geographic area;

wherein,

synthesizing absolute measurements includes using a plant growth model to propagate ground-based data forward or backward in time as needed to compare it with non-contemporaneous aerial data.

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 plant growth model being a linear model.

15. A system for making calibrated 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;

wherein,

synthesizing absolute measurements includes using a plant growth model to propagate ground-based data forward or backward in time as needed to compare it with non-contemporaneous aerial data.

16. The system of claim 15, the aerial data obtained from a satellite.

17. The system of claim 15, the aerial data obtained from an airplane.

18. The system of claim 15, the agricultural metric being normalized difference vegetative index.

19. The system of claim 15, the agricultural metric being a reflectance-based vegetative index.

20. The system of claim 15, 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.

21. The system of claim 15, the additional spatial agricultural data being a soil data map.

22. The system of claim 15, the additional spatial agricultural data being a crop data map.

23. The system of claim 15, the additional spatial agricultural data being climate data.

24. The system of claim 15, the chemicals being fertilizers.

25. The system of claim 15, the chemicals being pesticides or herbicides.

26. The system of claim 15, the prescription based on an agricultural algorithm having an agricultural metric and climate data as inputs.

27. The system of claim 26, the agricultural metric being normalized difference vegetative index and the climate data including growing degree days since planting.

28. The system of claim 15, the plant growth model being a linear model.