Interaction management in an online agricultural system

Patent metadata

Field	Value
Publication number	US-11915329-B2
Application number	US-202117468280-A
Country	US
Kind code	B2
Filing date	Sep 7, 2021
Priority date	Apr 24, 2018
Publication date	Feb 27, 2024
Grant date	Feb 27, 2024

Abstract

An online agricultural system manages and optimizes interactions of entities within the system to enable the execution of transaction and the transportation of crop products. The online agricultural system accesses historic and environmental data describing factors that may impact crop product transactions and/or transportation to determine market prices for crop products and crop product transportation. Responsive to receiving a request from an entity, the online agricultural system determines an optimal transaction for the entity, such as a price for selling a crop product, an available crop product for purchase, or a transportation opportunity to transport a crop product.

First claim

What is claimed is:

1. A method for training and applying a machine-learned model in an online agricultural system comprising:
generating, for each of a set of crop producers, a crop product listing within an online agricultural system for a crop product type, the crop product listing including a reported quality specification of the crop product, a quantity of the crop product, and a location of the crop product;
generating a training set of data comprising remote sensor data corresponding to the crop product type and associated historic quality specification data corresponding to the crop product type;
training a machine-learned model configured to predict a quality specification for the crop product type based on remote sensor data corresponding to the crop product type using the training set of data;
receiving, from a prospective acquiring entity, a request to acquire the crop product, the request including a quantity requirement of the crop product, a quality requirement of the crop product, and a delivery location for the crop product; and
in response to identifying one or more crop product listings that satisfy the quality requirement included within the received request:
accessing remote sensing data from one or more remote sensors corresponding to the locations included within the identified crop product listings;
applying the trained machine-learned model to the accessed remote sensing data to verify the reported quality specification included within the identified crop product listings;
for each of the identified crop product listings, calculating a supplier trustworthiness based on a difference between the reported quality specification included within the identified crop product listing and a corresponding verified quality specification; and
in response to the supplier trustworthiness score for each of the identified crop product listings being above a threshold score, modifying an interface of a device of the prospective acquiring entity to display the identified crop product listings.

2. The method of claim 1 , further comprising:
in response to a request from the prospective acquiring entity to acquire crop products associated with one or more of the displayed crop product listings, automatically arranging for a transfer of possession of the crop products to the prospective acquiring entity.

3. The method of claim 2 , wherein arranging for the transfer of possession of the crop products comprises one of: automatically sending transportation instructions to a transportation entity, confirming that one or more of the crop producers associated with the crop products are responsible for the transfer of possession, and confirming that the prospective acquiring entity is responsible for the transfer of possession.

4. The method of claim 3 , wherein the transportation instructions include locations of the crop products, the delivery location, and one or more routes from the locations of the crop products and the delivery location.

5. The method of claim 1 , wherein a reported quality specification of a crop product included within a crop product listing includes one or more of: a variety, a genetic trait or lack thereof, a genetic modification or lack thereof, a genomic edit or lack thereof, an epigenetic signature or lack thereof, a moisture content, a protein content, a carbohydrate content, an ash content, a fiber content, a fiber quality, a fat content, an oil content, a color, a whiteness, a weight, a transparency, a hardness, a percent chalky grains, a proportion of corneous endosperm, a presence or absence of foreign matter, a number or percentage of broken kernels, a number or percentage of kernels with stress cracks, a falling number, a farinograph, an adsorption of water, a milling degree, an immature grains, a kernel size distribution, an average grain, a length, an average grain breadth, a kernel volume, a density, an L/B ratio, a wet gluten, a sodium dodecyl, a sulfate sedimentation, toxin levels, and damage levels.

6. The method of claim 1 , wherein a reported quality specification of a crop product included within a crop product listing includes one or more attributes of a production method of a crop product or an environment in which the crop product was produced comprising one or more of: a soil type, a soil chemistry, a soil structure, a climate, weather, a magnitude or frequency of weather events, a soil or air temperature, a soil or air moisture, degree days, a measure of rain, an irrigation type, a tillage frequency, a cover crop (present and/or historical), a crop rotation, organic grown, shade grown, greenhouse grown, levels and types of fertilizer use, levels and types of chemical use, levels and types of herbicide use, pesticide-free grown, levels and types of pesticides use, no-till grown, fair wage grown, a geography of production (for example, country of origin, American Viticultural Area, mountain grown), and pollution-free grown.

7. The method of claim 1 , wherein a reported quality specification of a crop product identifies one or more attributes of how the crop product is stored comprising one or more of: a type of storage, environment conditions of the storage, a preservation type, and a length of time of storage.

8. The method of claim 1 , wherein a reported quality specification of a crop product identifies a grading or certification by an organization or agency.

9. The method of claim 1 , wherein a reported quality specification of a crop product identifies whether the crop product was grown using carbon neutral production.

10. The method of claim 1 , wherein a reported quality specification of a crop product identifies a level or duration of carbon sequestration.

11. The method of claim 1 , wherein one or both of the location of a crop product and the destination location comprise one or more of: a field boundary, a production location, and a storage location.

12. The method of claim 1 , wherein a crop product comprises one or more of: an unprocessed crop, a crop that has not been harvested, or a crop that has been harvested.

13. The method of claim 1 , wherein one or more of the crop product type, the quantity of the crop product, and the reported quality specification of the crop product included within a crop listing is inferred from remote sensing data.

14. The method of claim 1 , wherein the remote sensing data comprises satellite imagery.

15. A system for training and applying a machine-learned model in an online agricultural system comprising:
a non-transitory computer-readable storage medium storing executable instructions that, when executed, cause the system to perform steps comprising:
generating, for each of a set of crop producers, a crop product listing within an online agricultural system for a crop product type, the crop product listing including a reported quality specification of the crop product, a quantity of the crop product, and a location of the crop product;
generating a training set of data comprising remote sensor data corresponding to the crop product type and associated historic quality specification data corresponding to the crop product type;
training a machine-learned model configured to predict a quality specification for the crop product type based on remote sensor data corresponding to the crop product type using the training set of data;
receiving, from a prospective acquiring entity, a request to acquire the crop product, the request including a quantity requirement of the crop product, a quality requirement of the crop product, and a delivery location for the crop product; and
in response to identifying one or more crop product listi

Assignees

• Indigo Ag Inc

Inventors

• Perry David Patrick
• Knight Barry Loyd
• Jeck Eric Michael
• Raymond Rachel Ariel
• Rajdev Neal Hitesh
• Von Maltzahn Geoffrey Albert
• Berendes Robert
• Post Nathan
• Sheets-Poling Philip Gabriel
• Connor Rodney
• Hennek Jonathan
• Mullins Ean Shaughnessy Wahl

Classifications

• G06Q50/02 Primary

  Agriculture; Fishing; Forestry; Mining · CPC title

• G01W1/10

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G05D1/0214

  in accordance with safety or protection criteria, e.g. avoiding hazardous areas (monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13) · CPC title

• G06F3/0482

  Interaction with lists of selectable items, e.g. menus · CPC title

• G06F9/451

  Execution arrangements for user interfaces · CPC title