Wide-area agricultural monitoring and prediction

• US 20120101784A1
• Filed: 10/25/2010
• Published: 04/26/2012
• Est. Priority Date: 10/25/2010
• Status: Abandoned Application

• Current Assignee: Trimble Navigation Limited (Trimble Inc.)
• Original Assignee: Trimble Navigation Limited (Trimble Inc.)
• Inventors: Mayfield, Ted E., Ulman, Morrison, Lindores, Robert J.
• Application Number:
  US12/911,046
• Publication Number:
  US 20120101784A1
• Time in Patent Office: Days
• US Class Current: 703/2
• 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:

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.

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

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.

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.