Granted Patent B2

US 12,211,109 · App. 18/134,523 · Granted Jan 28, 2025

Inventors: Christina Bogdan (San Francisco, CA); Jason Kendrick Bull (Wildwood, MO); Nicholas Charles Cizek (Stanford, CA); Tonya Ehlmann (Saint Peters, MO); Morrison Jacobs (San Francisco, CA); Moslem Ladoni (Dublin, CA); Hunter Merrill (Saint Louis, MO); Timothy Reich (Ballwin, MO); Brandon Rinkenberger (Chesterfield, MO); Aaron E. Robinson (San Francisco, CA); Thomas Gene Ruff (Wildwood, MO); Doug Sauder (Livermore, CA); Allan Trapp (Saint Louis, MO); Daniel Williams (Sacramento, CA)

Assignee: CLIMATE LLC

G06Q50/02G06Q10/06313G06Q10/06375A01C21/005A01C21/007

Abstract

Claims (72)

1. A computer system comprising:

one or more processors of an agricultural intelligence computing system;

a digital electronic memory coupled to the one or more processors and storing executable instructions which, when executed by the one or more processors, cause performance of:

generating a trial recommendation for a particular agricultural field, the trial recommendation defining one or more management practices for the particular agricultural field;

based on field data for the particular agricultural field, computing a plurality of yield probabilities for the particular agricultural field, each of the yield probabilities specific to one yield value and including a probability of the particular agricultural field producing the one yield value in the particular field based on implementing the trial recommendation;

identifying a yield projection for the particular agricultural field based on a selected at least one of the plurality of yield probabilities;

generating a plurality of outcome-based values for the particular agricultural field, based on the yield projection;

computing crop values for each of the outcome-based values and a bushel per acre value;

generating and causing displaying a graphical user interface at a computer display device of a field manager computing device, the graphical user interface including an interactive sliding widget having a position indicative of the bushel per acre value, the graphical user interface dynamically including each of the plurality of outcome-based values and the crop values for the particular agricultural field, based on the position of the interactive sliding widget;

receiving user input changing the position of the interactive sliding widget in the graphical user interface, to change the bushel per acre value to an updated bushel per acre value;

in response to the user input changing the position of the interactive sliding widget, computing an updated crop value for each of the outcome-based values based on the changed bushel per acre value associated with the changed position of the interactive sliding widget; and

causing dynamically displaying, to the field manager computing device, the updated crop value for the updated bushel per acre value for each of the outcome-based values in the graphical user interface, in place of the crop values for the bushel per acre value.

2. The system of claim 1 , wherein the executable instructions, when executed by the one or more processors, further cause performance of:

receiving a selection of a particular outcome-based value of the plurality of outcome-based values, whereby the trial recommendation consistent with the selected outcome-based value defines a trial for the particular agricultural field;

receiving application data for the particular agricultural field;

based, at least in part, on the application data, determining that the particular agricultural field is in compliance with the trial;

receiving yield values for the particular agricultural field; and

based, at least in part, on the particular outcome-based value and the yield values, computing a benefit value for the trial.

3. The system of claim 2 , wherein the executable instructions, when executed by the one or more processors, further cause performance of:

generating, based on the application data for the particular agricultural field, a plurality of data layers for the agricultural field comprising a buffer layer, a treatment layer, a quality control layer and a planting data layer; and

determining that the particular agricultural field is in compliance with the trial by evaluating the application data with respect to the plurality of data layers for the agricultural field.

4. The system of claim 3 , wherein the quality control layer identifies one or more of edge passes, end passes, point rows, or operational abnormalities.

5. The system of claim 1 , wherein the executable instructions, when executed by the one or more processors, further cause performance of:

receiving a selection of a particular outcome-based value of the plurality of outcome-based values, whereby the trial recommendation consistent with the selected outcome-based value defines a trial for the particular agricultural field;

receiving application data for the particular agricultural field;

based, at least in part, on the application data and the trial recommendation, determining that the particular agricultural field is out of compliance with the trial;

updating the trial recommendation to create compliance with the trial; and

causing dynamically displaying of the updated trial recommendation to the computer display device of the field manager computing device associated with the particular agricultural field.

6. The system of claim 1 , wherein generating the trial recommendation for the one or more fields includes computing a short length variability for the agricultural field and identifying locations for implementing the trial based, at least in part, on the short length variability for the agricultural field.

7. The system of claim 1 , wherein the executable instructions, when executed by the one or more processors, further cause performance of:

using previous yield data for a plurality of agricultural fields, training a digital model of crop yield to predict parameters for a probability distribution of yield;

using previous yield data for the particular agricultural field, computing parameters for the probability distribution of yield for the agricultural field from the trained digital model of crop yield; and

computing the plurality of yield probabilities from the probability distribution of yield for the agricultural field.

8. The system of claim 1 , wherein the one or more management practices for the particular agricultural field differ from one or more previous management practices for the particular agricultural field.

9. The system of claim 1 , wherein the executable instructions, when executed by the one or more processors, further cause performance of:

determining that the particular agricultural field is not in compliance with a trial defined by the trial recommendation; and

in response to determining that the particular agricultural field is not in compliance with the trial, transmitting a warning to the computer display device of the field manager computing device associated with the particular agricultural field.

10. A computer-implemented method comprising:

generating, by an agricultural intelligence computing system, a trial recommendation for a particular agricultural field, the trial recommendation defining one or more management practices for the particular agricultural field;

based on a field data for the particular agricultural field, computing, by the agricultural intelligence computing system, a plurality of yield probabilities for the particular agricultural field, each of the yield probabilities specific to one yield value and including a probability of the particular agricultural field producing the one yield value in the particular field based on implementing the trial recommendation;

identifying, by the agricultural intelligence computing system, a yield projection for the particular agricultural field based on a selected at least one of the plurality of yield probabilities;

generating, by the agricultural intelligence computing system, a plurality of outcome-based values for the particular agricultural field, based on the yield projection;

computing, by the agricultural intelligence computing system, crop values for each of the outcome-based values and a bushel per acre value;

generating, by the agricultural intelligence computing system, and causing displaying, to a field manager computing device, a graphical user interface at a computer display device of the field manager computing device, the graphical user interface including an interactive sliding widget having a position indicative of the bushel per acre value, the graphical user interface dynamically including each of the plurality of outcome-based values and the crop values for the particular agricultural field, based on the position of the interactive sliding widget; and then

receiving user input changing the position of the interactive sliding widget in the graphical user interface, to change the bushel per acre value to an updated bushel per acre value;

in response to the user input changing the position of the interactive sliding widget, computing, by the agricultural intelligence computing system, an updated crop value for each of the outcome-based values and based on the changed bushel per acre value associated with the changed position of the interactive sliding widget; and

causing dynamically displaying, to the field manager computing device, the updated crop value for the updated bushel per acre value for each of the outcome-based values in the graphical user interface, in place of the crop values for the bushel per acre value.

11. The method of claim 10 , further comprising:

receiving a selection of a particular outcome-based value of the plurality of outcome-based values, whereby the trial recommendation consistent with the selected outcome-based value defines a trial for the particular agricultural field;

receiving application data for the particular agricultural field;

based, at least in part, on the application data, determining that the particular agricultural field is in compliance with the trial;

receiving yield values for the particular agricultural field; and

based, at least in part, on the particular outcome-based value and the yield values, computing a benefit value for the trial.

12. The method of claim 11 , further comprising

generating, based on the application data for the particular agricultural field, a plurality of data layers for the agricultural field comprising a buffer layer, a treatment layer, a quality control layer and a planting data layer; and

determining that the particular agricultural field is in compliance with the trial by evaluating the application data with respect to the plurality of data layers for the agricultural field.

13. The method of claim 12 , wherein the quality control layer identifies one or more of edge passes, end passes, point rows, or operational abnormalities.

14. The method of claim 10 , further comprising:

receiving a selection of a particular outcome-based value of the plurality of outcome-based values, whereby the trial recommendation consistent with the selected outcome-based value defines a trial for the particular agricultural field;

receiving application data for the particular agricultural field;

based, at least in part, on the application data and the trial recommendation, determining that the particular agricultural field is out of compliance with the trial;

updating the trial recommendation to create compliance with the trial; and

causing dynamically displaying of the updated trial recommendation to the computer display device of the field manager computing device associated with the particular agricultural field.

15. The method of claim 14 , wherein generating the trial recommendation for the one or more fields includes computing a short length variability for the agricultural field and identifying locations for implementing the trial based, at least in part, on the short length variability for the agricultural field.

16. The method of claim 10 , further comprising:

using previous yield data for a plurality of agricultural fields, training a digital model of crop yield to predict parameters for a probability distribution of yield;

using previous yield data for the particular agricultural field, computing parameters for the probability distribution of yield for the agricultural field from the trained digital model of crop yield; and

computing the plurality of yield probabilities from the probability distribution of yield for the agricultural field.

17. The method of claim 10 , wherein the one or more management practices for the particular agricultural field differ from one or more previous management practices for the particular agricultural field.

18. The method of claim 10 , further comprising:

determining that the particular agricultural field is not in compliance with a trial defined by the trial recommendation; and

in response to determining that the particular agricultural field is not in compliance with the trial, transmitting a warning to the computer display device of the field manager computing device associated with the particular agricultural field.

Assignments (4)

Continuity (3)

References Cited (42)