Information translation in an online agricultural system

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.

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;   identifying a crop product listing that satisfies the quality requirement included within the received request;   applying the trained machine-learned model to remote sensor data from a location included within the identified crop product listing to predict a quality specification associated with the identified crop product listing; and   modifying one or more of the delivery location, a delivery time window, and the prospective acquiring entity based on the predicted quality specification associated with the identified crop product listing.   
     
     
         2 . The method of  claim 1 , wherein the remote sensor data comprises one or both of historic and current satellite imagery corresponding to the location included within the identified crop product listing or to locations with one or more characteristics in common with the location included within the identified crop product listing. 
     
     
         3 . The method of  claim 1 , wherein the remote sensor data comprises data from sensors located at the location included within the identified crop product listing, the sensors comprising fixed-location sensors or sensors coupled to movable equipment or vehicles. 
     
     
         4 . The method of  claim 1 , wherein the remote sensor data comprises data from sensors coupled to aerial vehicles. 
     
     
         5 . The method of  claim 1 , wherein the predicted quality specification comprises 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 LB ratio, a wet gluten, a sodium dodecyl, a sulfate sedimentation, toxin levels, and damage levels. 
     
     
         6 . The method of  claim 1 , wherein the predicted quality specification identifies one or more attributes of a predicted crop production method or production environment 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, 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, pollution-free grown, and carbon neutral grown. 
     
     
         7 . The method of  claim 1 , wherein the predicted quality specification identifies one or more attributes of a crop product storage method 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 the predicted quality specification comprises a predicted grading or certification by an organization or agency. 
     
     
         9 . The method of  claim 1 , wherein one or more of the delivery location, the delivery time window, and the prospective acquiring entity are modified without informing the crop producer associated with the identified crop product listing. 
     
     
         10 . A system comprising:
 a non-transitory computer-readable storage medium storing executable instructions that, when executed, cause steps to be performed 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; 
 identifying a crop product listing that satisfies the quality requirement included within the received request; 
 applying the trained machine-learned model to remote sensor data from a location included within the identified crop product listing to predict a quality specification associated with the identified crop product listing; and 
 modifying one or more of the delivery location, a delivery time window, and the prospective acquiring entity based on the predicted quality specification associated with the identified crop product listing; and 
   a hardware processor configured to execute the executable instructions.   
     
     
         11 . The system of  claim 10 , wherein the remote sensor data comprises one or both of historic and current satellite imagery corresponding to the location included within the identified crop product listing or to locations with one or more characteristics in common with the location included within the identified crop product listing. 
     
     
         12 . The system of  claim 10 , wherein the remote sensor data comprises data from sensors located at the location included within the identified crop product listing, the sensors comprising fixed-location sensors or sensors coupled to movable equipment or vehicles. 
     
     
         13 . The system of  claim 10 , wherein the remote sensor data comprises data from sensors coupled to aerial vehicles. 
     
     
         14 . The system of  claim 10 , wherein the predicted quality specification comprises 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 LB ratio, a wet gluten, a sodium dodecyl, a sulfate sedimentation, toxin levels, and damage levels. 
     
     
         15 . The system of  claim 10 , wherein the predicted quality specification identifies one or more attributes of a predicted crop production method or production environment 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, 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, pollution-free grown, and carbon neutral grown. 
     
     
         16 . The system of  claim 10 , wherein the predicted quality specification identifies one or more attributes of a crop product storage method comprising one or more of: a type of storage, environment conditions of the storage, a preservation type, and a length of time of storage. 
     
     
         17 . The system of  claim 10 , wherein the predicted quality specification comprises a predicted grading or certification by an organization or agency. 
     
     
         18 . The system of  claim 10 , wherein one or more of the delivery location, the delivery time window, and the prospective acquiring entity are modified without informing the crop producer associated with the identified crop product listing. 
     
     
         19 . A non-transitory computer-readable storage medium storing executable instructions for training and applying a machine-learned model in an online agricultural system, the instructions, when executed by a hardware processor, configured to cause the processor 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;   identifying a crop product listing that satisfies the quality requirement included within the received request;   applying the trained machine-learned model to remote sensor data from a location included within the identified crop product listing to predict a quality specification associated with the identified crop product listing; and   modifying one or more of the delivery location, a delivery time window, and the prospective acquiring entity based on the predicted quality specification associated with the identified crop product listing.   
     
     
         20 . The non-transitory computer-readable storage medium of  claim 19 , wherein one or more of the delivery location, the delivery time window, and the prospective acquiring entity are modified without informing the crop producer associated with the identified crop product listing.