Machine learning in agricultural planting, growing, and harvesting contexts

Patent metadata

Field	Value
Publication number	US-11263707-B2
Application number	US-201816057387-A
Country	US
Kind code	B2
Filing date	Aug 7, 2018
Priority date	Aug 8, 2017
Publication date	Mar 1, 2022
Grant date	Mar 1, 2022

Abstract

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

First claim

What is claimed is:

1. A method comprising:
accessing, for each of a plurality of portions of land, crop growth information describing 1) characteristics of the portion of land, 2) a first set of farming operations yet to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations;
identifying a cluster of portions of land associated with a threshold similarity;
applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the accessed crop growth information associated with the cluster of portions of land, the prediction model configured to output an optimized prediction, for a selected portion of land within the identified cluster of the portions of land, comprising:
1) a selected variety or type of crop to plant,
2) a second set of farming operations that can produce a second expected crop productivity, and
3) the second expected crop productivity;

wherein the prediction is based on inputs to the prediction model comprising:
1) the characteristics of the selected portion of land,
2) the first set of farming operations yet to be performed on the selected portion of land, and
3) the first expected crop productivity corresponding to the first set of farming operations yet to be performed on the selected portion of land; and

for the selected portion of land within the identified cluster of the portions of land, 1) modifying the first set of farming operations yet to be performed on the selected portion of land based on the selected variety or type of crop to plant and the second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations.

2. The method of claim 1 , wherein a first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency.

3. The method of claim 1 , wherein the threshold similarity identifies one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation.

4. The method of claim 1 , wherein the crop growth program is periodically modified in response to re-applying the prediction model to periodically accessed crop growth information.

5. The method of claim 1 , wherein the portions of land are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof.

6. The method of claim 1 , wherein the prediction model is applied to the accessed crop growth information in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event.

7. The method of claim 1 , wherein the plurality of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity.

8. The method of claim 1 , wherein the prediction model is applied to the accessed crop growth information in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the plurality of portions of land.

9. The method of claim 1 , wherein crop productivity is one or more of crop yield, profit, soil health, carbon sequestration, production at a particular date, and composition profile of crops.

10. The method of claim 1 , wherein the accessed crop growth information further describes one or more of: rainfall associated with the portion of land, canopy temperature associated with the portion of land, soil temperature of the portion of land, soil moisture of the portion of land, soil nutrients within the portion of land, soil type of the portion of land, topography within the portion of land, humidity associated with the portion of land, growing degree days associated with the portion of land, microbial community associated with the portion of land, pathogen presence associated with the portion of land, prior farming operations performed at the portion of land, prior crops grown at the portion of land, other historical field information associated with the portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling.

11. The method of claim 1 , wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation.

12. The method of claim 1 , wherein the accessed crop growth information is collected from one or more of: sensors located at a portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems.

13. The method of claim 1 , wherein the accessed crop growth is normalized, and wherein normalizing the crop growth information comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth informatio

Assignees

• Indigo Ag Inc

Inventors

• Perry David Patrick
• Von Maltzahn Geoffrey Albert
• Berendes Robert
• Jeck Eric Michael
• Knight Barry Loyd
• Raymond Rachel Ariel
• Trivisvavet Ponsi
• Wong Justin Y H
• Rajdev Neal Hitesh
• Meunier Marc-Cedric Joseph
• Leist Casey James
• Tadi Pranav Ram
• Flaherty Andrea Lee
• Brummitt Charles David
• Sinha Naveen Neil
• Lambert Jordan
• Hennek Jonathan
• Becco Carlos
• Allen Mark
• Bachner Daniel
• Derossi Fernando
• Lamont Ewan
• Lowenthal Rob
• Creagh Dan
• Abramson Steve
• Allen Ben
• Shankar Jyoti
• Moscardini Chris
• Crane Jeremy
• Weisman David
• Keating Gerard
• Moores Lauren
• Pate William

Classifications

• G06F18/217

  Validation; Performance evaluation; Active pattern learning techniques · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06T2207/30188

  Vegetation; Agriculture · CPC title

• G06N3/02

  Neural networks · CPC title

• G06Q30/02

  Marketing; Price estimation or determination; Fundraising · CPC title