Generating digital models of crop yield based on crop planting dates and relative maturity values

Patent metadata

Field	Value
Publication number	US-11375674-B2
Application number	US-202016916022-A
Country	US
Kind code	B2
Filing date	Jun 29, 2020
Priority date	Jan 7, 2016
Publication date	Jul 5, 2022
Grant date	Jul 5, 2022

Abstract

A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

First claim

What is claimed is:

1. A computer-implemented method for improving performance of a computing system used to model potential crop yield, the method comprising:
storing, at a server computer system, a model of potential crop yield, the model of potential crop yield comprising generated digital modeling data used to compute potential crop yield;
computing, from the model of potential crop yield, a particular potential crop yield for a particular field;
receiving, over a network at the server computer system, electronic digital data comprising a plurality of values representing trial data at a plurality of fields;
receiving, over the network at the server computer system, electronic digital data comprising a plurality of values representing observed agricultural events at the plurality of fields;
computing a modeled crop yield for each field of the plurality of fields based, at least in part, on the trial data, the observed agricultural events, and the model of potential crop yield;
receiving, over the network at the server computer system, electronic digital data comprising a plurality of values representing an observed crop yield for each field of the plurality of fields; and
generating an updated model of potential crop yield based, at least in part, on the modeled crop yield for each field of the plurality of fields and the observed crop yield for each field of the plurality of fields.

2. The computer-implemented method of claim 1 , wherein generating the updated model comprises adjusting coefficients of a particular predictor variable based, at least in part, on a difference between the modeled crop yield and the observed crop yield.

3. The computer-implemented method of claim 2 , wherein adjusting the coefficients comprises identifying a factor-specific effect on crop yield for a particular factor based, at least in part, on the trial data, and adjusting the coefficients corresponding to the particular factor.

4. The computer-implemented method of claim 3 , wherein the particular factor comprises one or more of a relative maturity and/or a planting date.

5. The computer-implemented method of claim 1 , wherein the model of potential crop yield models potential crop yield as a function of at least relative maturity, planting date, and location;
wherein the trial data corresponds to a trial where relative maturity and planting date are kept constant; and
wherein generating the updated model comprises computing a location dependence on yield based, at least in part, on the trial data and a difference between the observed crop yield and the modeled crop yield at each location of a plurality of locations.

6. The computer-implemented method of claim 5 , wherein the plurality of locations comprises the plurality of fields.

7. The computer-implemented method of claim 5 , wherein the plurality of locations comprises locations on a particular field of the plurality of fields.

8. The computer-implemented method of claim 1 , further comprising:
storing, in digital memory of the server computer system, crop relative maturity data comprising a plurality of seed types and a plurality of associated relative maturity values;
receiving, over the network at the server computer system, a proposed planting date for the particular field;
determining, from at least the updated model of potential crop yield and the proposed planting date for the particular field, a particular relative maturity value that maximizes a potential crop yield for the particular field;
identifying, in the crop relative maturity data, a particular seed type corresponding to the particular relative maturity value; and
sending, over the network to a field manager computing device, a recommendation of the particular seed type for the proposed planting date for the particular field.

9. The computer-implemented method of claim 1 , further comprising:
receiving, over the network at the server computer system, a proposed relative maturity value for the particular field;
determining, from at least the updated model of potential crop yield and the proposed relative maturity value for the particular field, a particular planting date that maximizes a potential crop yield for the particular field; and
sending, over the network to a field manager computing device, a recommendation of the particular planting date for the proposed relative maturity for the particular field.

10. The computer-implemented method of claim 1 , wherein the model of potential crop yield comprises a model generated from one or more of actual production history maps, relative maturity maps, and/or planting date maps.

11. A system comprising:
one or more processors; and
a memory storing instructions which, when executed by the one or more processors, cause the one or more processors to perform:
storing a model of potential crop yield, the model of potential crop yield comprising generated digital modeling data used to compute potential crop yield;
computing, from the model of potential crop yield, a particular potential crop yield for a particular field;
receiving, over a network, electronic digital data comprising a plurality of values representing trial data at a plurality of fields;
receiving, over the network, electronic digital data comprising a plurality of values representing observed agricultural events at the plurality of fields;
computing a modeled crop yield for each field of the plurality of fields based, at least in part, on the trial data, the observed agricultural events, and the model of potential crop yield;
receiving, over the network, electronic digital data comprising a plurality of values representing an observed crop yield for each field of the plurality of fields; and
generating an updated model of potential crop yield based, at least in part, on the modeled crop yield for each field of the plurality of fields and the observed crop yield for each field of the plurality of fields.

12. The system of claim 11 , wherein generating the updated model comprises adjusting coefficients of a particular predictor variable based, at least in part, on a difference between the modeled crop yield and the observed crop yield.

13. The system of claim 12 , wherein adjusting the coefficients comprises identifying a factor-specific effect on crop yield for a particular factor based, at least in part, on the trial data, and adjusting the coefficients corresponding to the particular factor.

14. The system of claim 13 , wherein the particular factor comprises one or more of a relative maturity and/or a planting date.

15. The system of claim 11 , wherein the model of potential crop yield models potential crop yield as a function of at least relative maturity, planting date, and location;
wherein the trial data corresponds to a trial where relative maturity and planting date are kept constant; and
wherein generating the updated model comprises computing a location dependence on yield based, at least in part, on the trial data and a difference between the observed crop yield and the modeled crop yield at each location of a plurality of locations.

16. The system of claim 15 , wherein the plurality of locations comprises the plurality of fields.

17. The system of claim 15 , wherein the plurality of locations comprises locations on a particular field of the plurality of fields.

18. The system of claim 11 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to perform:
storing crop relative maturity data comprising a plurality of seed types and a plurality

Assignees

• Climate Llc

Inventors

• Xu Ying
• Andrejko Erik

Classifications

• A01C21/005 Primary

  Following a specific plan, e.g. pattern · CPC title

• G06Q50/02

  Agriculture; Fishing; Forestry; Mining · CPC title

• H04L43/045

  for graphical visualisation of monitoring data · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title