Systems for adjusting agronomic inputs using remote sensing, and related apparatus and methods

• US 10,319,050 B2
• Filed: 09/11/2017
• Issued: 06/11/2019
• Est. Priority Date: 09/09/2016
• Status: Active Grant

• Current Assignee: CiBO Technologies Inc.
• Original Assignee: CiBO Technologies Inc.
• Inventors: Richt, Ryan
• Primary Examiner(s): Yang, Wei Wen
• Application Number:
  US15/701,347
• Publication Number:
  US 20180075545A1
• Time in Patent Office: 638 Days
• Field of Search: None
• CPC Class Codes: A01B 79/005 Precision agriculture
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/02 Agriculture; Fishing; Forestry; Mining
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30188 Vegetation; Agriculture
• CPC Class Codes: G06V 20/188 Vegetation

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for using remote sensing data to infer agronomic inputs to an agronomic simulation model.

Claims

1. A method comprising:

identifying, based on data from an agronomic simulation model, a first indication regarding existence of a first agricultural characteristic in a particular portion of a first geographic region;

receiving one or more images of the first geographic region, the received images having been obtained using one or more remote sensing devices;

identifying, based on the received images, a second indication regarding existence of the first agricultural characteristic in the particular portion of the first geographic region;

determining that the second indication is distinct from the first indication;

in response to determining that the second indication is distinct from the first indication, inferring one or more inputs to the agronomic simulation model based on the received images to account for the existence of the first agricultural characteristic as indicated by the second indication, and

generating a first predicted value for crop yield in the first geographic region by executing the agronomic simulation model with the inferred inputs, wherein the first predicted value for the crop yield is more accurate than a second predicted value for the crop yield generated by executing the agronomic simulation model without the inferred inputs,

wherein inferring one or more inputs to the model includes adjusting one or more parameters of the model and/or one or more agronomic inputs to the model to account for the existence of the first agricultural characteristic, and

wherein adjusting the parameters of the model and/or the agronomic inputs to the model to account for the existence of the first agricultural characteristic includes iteratively adjusting the parameters and/or the agronomic inputs and executing the agronomic simulation model until output data provided by the model predict the existence of the first agricultural characteristic.

2. The method of claim 1, further comprising:

identifying, based on second data from an agronomic simulation model, a third indication regarding existence of a second agricultural characteristic in a particular portion of a second geographic region;

receiving remote sensing data associated with the second geographic region, the received remote sensing data having been obtained using one or more remote sensing devices;

identifying, based on the received remote sensing data, a fourth indication regarding existence of the second agricultural characteristic in the particular portion of the second geographic region;

determining that the fourth indication is substantially in accordance with the third indication; and

based on determining that the fourth indication is substantially in accordance with the third indication, confirming a validity of the third indication regarding the existence of the second agricultural characteristic.

3. The method of claim 1, wherein the first agricultural characteristic includes pollination, evapotranspiration and/or tasseling.

4. The method of claim 1, wherein the images comprise images generated from infrared measurements, thermal measurements, visible light measurements, near-infrared measurements, measurements of ultraviolet light and other forms of electromagnetic radiation, and/or aerially collected remote sensing data.

5. The method of claim 1, wherein identifying, based on data from the agronomic simulation model, a first indication regarding the existence of a first agricultural characteristic in a particular portion of a first geographic region includes:

providing geographic data, other than the received images, that identifies the particular portion of the first geographic region to an agronomic simulation model; and

receiving an output from the agronomic simulation model that includes data identifying one or more agricultural characteristics that the agronomic simulation model predicts as existing within the particular portion of the first geographic region.

6. The method of claim 5, wherein the one or more agricultural characteristics are predicted by the agronomic simulation model based on an evaluation of rainfall, soil hydraulic conductivity, and elevation.

7. The method of claim 1, wherein receiving images associated with the first geographic region includes:

receiving images of the first geographic region captured by one or more cameras.

8. The method of claim 7, wherein identifying, based on the received images, a second indication regarding the existence of the first agricultural characteristic in the particular portion of the first geographic region includes:

analyzing the one or more images of the first geographic region to determine whether the one or more images include an indication of the existence of the first agricultural characteristic.

9. The method of claim 8, wherein the first agricultural characteristic includes ponding of water, tasseling and/or canopy growth.

10. The method of claim 1, wherein the one or more remote sensing devices include a camera coupled to a plane, drone, or satellite.

11. The method of claim 1, wherein the first agricultural characteristic includes ponding of water, dead plants, yellow plants, a canopy, pollination, evapotranspiration, and/or tasseling.

12. The method of claim 1, wherein determining that the second indication is distinct from the first indication includes determining that the second indication based on the images identifies at least one agronomic characteristic that is not modeled by the agronomic simulation model, and wherein the method further comprises updating the agronomic simulation model to model the identified at least one agronomic characteristic.

13. The method of claim 1, wherein the first indication regarding the existence of a first agricultural characteristic in a particular portion of a first geographic region includes data indicating the non-existence of the first agricultural characteristic, and wherein the second indication regarding the existence of the first agricultural characteristic in the particular portion of the first geographic region includes data indicating the existence of the first agricultural characteristic.

14. A method of using remote sensing data to infer one or more inputs to an agronomic simulation model, the method comprising:

receiving remote sensing data associated with a first geographic region, the received remote sensing data having been obtained using one or more remote sensing devices;

determining, based on the received remote sensing data, that one or more portions of the first geographic region are associated with a particular agricultural characteristic;

determining whether the particular agricultural characteristic is produced by one or more biotic factors;

in response to determining that the particular agricultural characteristic is produced by the one or more biotic factors, inferring one or more inputs to the agronomic simulation model to account for the one or more biotic factors; and

generating a first predicted value for crop yield in the first geographic region by executing the agronomic simulation model with the inferred inputs, wherein the first predicted value for the crop yield is more accurate than a second predicted value for the crop yield generated by executing the agronomic simulation model without the inferred inputs,

wherein inferring one or more inputs to the agronomic simulation model includes adjusting one or more parameters of the agronomic simulation model to account for the one or more biotic factors, and

wherein adjusting the parameters of the model to account for the biotic factors includes iteratively adjusting the parameters and executing the agronomic simulation model until output data provided by the model predict existence of the particular agricultural characteristic in the first geographic region.

15. The method of claim 14, further comprising:

for the particular agricultural characteristic, calculating, by the agronomic simulation model, another agricultural characteristic, with the particular agricultural characteristic being attributable to the other agricultural characteristic.

16. The method of claim 15, wherein calculating the other agricultural characteristic includes back-calculating the other agricultural characteristic.

17. The method of claim 15, wherein the particular agricultural characteristic comprises an emergence date and wherein the other agricultural characteristic comprises a planting date.

18. The method of claim 15, wherein the particular agricultural characteristic is indicative of ponding and wherein the other agricultural characteristic is indicative of soil hydraulic conductivity.

19. The method of claim 14, wherein the particular agricultural characteristic includes pollination, tasseling, evapotranspiration, a canopy, and/or a plant stand count.

20. The method of claim 14, wherein receiving remote sensing data associated with the first geographic region includes receiving data indicative of one or more color images of the first geographic region, the color images having been captured by one or more cameras, and

wherein determining, based on the received remote sensing data, that one or more portions of the first geographic region are associated with a particular agricultural characteristic includes analyzing the one or more color images of the first geographic region to determine whether the one or more color images indicate an existence or value of the particular agricultural characteristic.

21. The method of claim 20, wherein the particular agricultural characteristic is an indication of agricultural stress.

22. The method of claim 20, wherein analyzing the one or more color images of the first geographic region to determine whether the one or more color images indicate an existence or value of a particular agricultural characteristic includes:

analyzing the one or more color images of the first geographic region to determine whether the one or more color images include one or more indications of yellow vegetation.

23. The method of claim 14, wherein determining whether the particular agricultural characteristic is produced by one or more biotic factors includes:

providing geographic data, other than the received remote sensing data, that identifies the one or more portions of the first geographic region associated with the particular agricultural characteristic to an agronomic simulation model; and

receiving an output from the agronomic simulation model that includes data indicating whether the particular agricultural characteristic associated with each of the one or more respective portions of the first geographic region is caused by one or more non-biotic factors.

24. The method of claim 23, further comprising:

based on a determination that the output from the agronomic simulation model indicates that the particular agricultural characteristic associated with each of the one or more respective portions of the first geographic region is not caused by one or more non-biotic factors, determining that the particular agricultural characteristic is caused by one or more biotic factors.

25. The method of claim 23, further comprising:

based on a determination that the output from the agronomic simulation model indicates that the particular agricultural characteristic associated with each of the one or more respective portions of the first geographic region is caused by one or more non-biotic factors, determining that the particular agricultural characteristic is not caused by one or more biotic factors.

26. The method of claim 14, wherein the remote sensing data comprises infrared remote sensing data, thermal measurements, and/or aerially collected remote sensing data.

27. The method of claim 14, wherein the biotic factors include existence of fungi, insects, and/or weeds, and the non-biotic factors include soil pH, soil nitrogen levels, soil consistency, soil depth, rainfall, phosphorus levels, and/or elevation.

28. A system comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising;

identifying, based on data from an agronomic simulation model, a first indication regarding existence of a first agricultural characteristic in a particular portion of a first geographic region;

receiving one or more images of the first geographic region, the received images having been obtained using one or more remote sensing devices;

identifying, based on the received images, a second indication regarding existence of the first agricultural characteristic in the particular portion of the first geographic region;

determining that the second indication is distinct from the first indication;

in response to determining that the second indication is distinct from the first indication, inferring one or more inputs to the agronomic simulation model based on the received images to account for the existence of the first agricultural characteristic as indicated by the second indication; and

generating a first predicted value for crop yield in the first geographic region by executing the agronomic simulation model with the inferred inputs, wherein the first predicted value for the crop yield is more accurate than a second predicted value for the crop yield generated by executing the agronomic simulation model without the inferred inputs,

wherein inferring one or more inputs to the model includes adjusting one or more parameters of the model and/or one or more agronomic inputs to the model to account for the existence of the first agricultural characteristic, and

wherein adjusting the parameters of the model and/or the agronomic inputs to the model to account for the existence of the first agricultural characteristic includes iteratively adjusting the parameters and/or the agronomic inputs and executing the agronomic simulation model until output data provided by the model predict the existence of the first agricultural characteristic.

29. A system comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising;

receiving remote sensing data associated with a first geographic region, the received remote sensing data having been obtained using one or more remote sensing devices;

determining, based on the received remote sensing data, that one or more portions of the first geographic region are associated with a particular agricultural characteristic;

determining whether the particular agricultural characteristic is produced by one or more biotic factors;

in response to determining that the particular agricultural characteristic is produced by the one or more biotic factors, inferring one or more inputs to the agronomic simulation model to account for the one or more biotic factors; and

generating a first predicted value for crop yield in the first geographic region by executing the agronomic simulation model with the inferred inputs, wherein the first predicted value for the crop yield is more accurate than a second predicted value for the crop yield generated by executing the agronomic simulation model without the inferred inputs,

wherein inferring one or more inputs to the agronomic simulation model includes adjusting one or more parameters of the agronomic simulation model to account for the one or more biotic factors, and

wherein adjusting the parameters of the model to account for the biotic factors includes iteratively adjusting the parameters and executing the agronomic simulation model until output data provided by the model predict existence of the particular agricultural characteristic in the first geographic region.