Satellite-based agricultural modeling

Patent metadata

Field	Value
Publication number	US-11170453-B2
Application number	US-201916450821-A
Country	US
Kind code	B2
Filing date	Jun 24, 2019
Priority date	Apr 24, 2018
Publication date	Nov 9, 2021
Grant date	Nov 9, 2021

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 comprising:
generating a first training set of data comprising historic crop information and current crop information representative of a first crop product;
training a first machine-learned model configured to predict a future characteristic of the first crop product using the training set of data;
generating a second training set of data comprising remote sensor data corresponding to the crop product type of the first crop product and associated historic quality specification data corresponding to the crop product type of the first crop product;
training a second machine-learned model configured to predict a quality specification for the first crop product based on remote sensor data corresponding to the first crop product using the second training set of data;
for each of a plurality of crop producers:
receiving a first request to list a first crop product within an online agricultural system, the first request identifying a first location of the first crop product and a crop product type of the first crop product;
applying the first machine-learned model to the first location of the first crop product and the crop product type to predict a first future characteristic of the first crop product;
modifying a first user interface presented to the crop producer to include the predicted first future characteristic, the user interface requesting one or both of a first crop product price and a first quantity of the first crop product; and
determining a first quality specification for the first crop product listed within the first request by applying the second machine-learned model to remote sensor data corresponding to the first crop product; and

for each of a plurality of prospective acquiring entities:
receiving a second request to acquire a second crop product, the second request identifying a second quality requirement of the second crop product and at least the crop product type of the second crop product, a second quantity of the second crop product, a second crop product price, and a second location to which the second crop product is to be delivered;
monitoring, by a server in real-time, the first quality specification of each first request received from a crop producer to identify a set of first requests with first quality specifications that satisfy the second quality requirement of the second crop product identified by the second request; and
modifying a second user interface presented to the prospective acquiring entity to include for one or more first requests in the identified set of first requests: the corresponding predicted first future characteristic, the corresponding first crop product price, the corresponding first quantity of the first crop product, the corresponding first location of the first crop product, and the corresponding first quality specification of the first crop product.

2. The method of claim 1 , wherein the first location of the first crop product comprises one of: a field boundary, a production location of the first crop product, and a storage location of the first crop product.

3. The method of claim 1 , further comprising, for each of the plurality of crop producers, obtaining environmental data for the first location of the first crop product, and wherein the environmental data comprises current or historic weather data and/or one or more soil characteristics.

4. The method of claim 1 , wherein, for at least one of the plurality of crop producers, the first crop product is a crop that has not been harvested, and wherein one or more of the type of the first crop product and the first quantity of the first crop product is inferred from the remote sensing data.

5. The method of claim 1 , wherein the second interface is modified to further include an expected distribution of prices or an expected average price of the second crop product for the prospective acquiring entity to acquire the second quantity of the second crop product.

6. The method of claim 1 , wherein the second interface is modified to further include a distribution of geographic locations from which the second crop product is expected to be acquired for the prospective acquiring entity to acquire the second quantity of the second crop product.

7. The method of claim 1 , wherein the remote sensor data corresponding to the first crop product is obtained from remote sensors in real-time during transportation of the first crop product.

8. The method of claim 1 , wherein the first location of the first crop product is the production location of the first crop product and the remote sensor data is satellite data representative of the production location of the first crop product.

9. The method of claim 1 , wherein the remote sensor data is obtained from a set of remote sensors including one or more of: GPS sensors, in-cargo sensors, hyperspectral sensors, NIR or visible spectroscopy sensors, temperature sensors, moisture sensors, humidity sensors, sensors to detect a presence of pests, and CO2 level sensors.

10. A non-transitory computer-readable storage medium storing executable instructions that, when executed, cause an online agricultural system to perform steps comprising:
generating a first training set of data comprising historic crop information and current crop information representative of a first crop product;
training a first machine-learned model configured to predict a future characteristic of the first crop product using the training set of data;
generating a second training set of data comprising remote sensor data corresponding to the crop product type of the first crop product and associated historic quality specification data corresponding to the crop product type of the first crop product;
training a second machine-learned model configured to predict a quality specification for the first crop product based on remote sensor data corresponding to the first crop product using the second training set of data;
for each of a plurality of crop producers:
receiving a first request to list a first crop product within the online agricultural system, the first request identifying a first location of the first crop product and a crop product type of the first crop product;
applying the first machine-learned model to the first location of the first crop product and the crop product type to predict a first future characteristic of the first crop product;
modifying a first user interface presented to the crop producer to include the predicted first future characteristic, the user interface requesting one or both of a first crop product price and a first quantity of the first crop product; and
determining a first quality specification for the first crop product listed within the first request by applying the second machine-learned model to remote sensor data corresponding to the first crop product; and

for each of a plurality of prospective acquiring entities:
receiving a second request to acquire a second crop product, the second request identifying a second quality requirement of the second crop product and at least the crop product type of the second crop product, a second quantity of the second crop product, a second crop product price, and a second location to which the second crop product is to be delivered;
monitoring, by a server in real-time, the first quality specification of each first request received from a crop producer to identify a set of first requests with first quality specifications that satisfy the second quality requirement of the second crop product identified by the second request; and
modifying a second user interface presented to the prospective acquiring entity to include for one or more first requests in the identified set of first requests

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
• Hennek Jonathan
• Mullins Ean Shaughnessy Wahl

Classifications

• G06V20/188

  Vegetation · CPC title

• G16Y10/05

  Agriculture · CPC title

• G06Q10/08345

  Pricing · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/087

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title