Satellite-based agricultural modeling

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 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; 
 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 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 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 . The method of  claim 1 , wherein a first quality specification or a second quality requirement comprises one or more physical or chemical attributes of a crop product comprising 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 absorption of water, a milling degree, a measure of 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, mycotoxin levels, and damage levels. 
     
     
         11 . The method of  claim 1 , wherein a first quality specification or a second quality requirement comprises 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 present or historical 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, country of origin, American Viticultural Area or origin, mountain grown, pollution-free grown, and carbon neutral grown. 
     
     
         12 . 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; 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. 
   
     
     
         13 . The computer-readable storage medium of  claim 12 , 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. 
     
     
         14 . The computer-readable storage medium of  claim 12 , 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. 
     
     
         15 . The computer-readable storage medium of  claim 12 , 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. 
     
     
         16 . The computer-readable storage medium of  claim 12 , wherein the 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. 
     
     
         17 . The computer-readable storage medium of  claim 12 , wherein the 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. 
     
     
         18 . The computer-readable storage medium of  claim 12 , 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. 
     
     
         19 . The computer-readable storage medium of  claim 12 , 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. 
     
     
         20 . The computer-readable storage medium of  claim 12 , 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. 
     
     
         21 . The computer-readable storage medium of  claim 12 , wherein a first quality specification or a second quality requirement comprises one or more physical or chemical attributes of a crop product comprising 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 absorption of water, a milling degree, a measure of 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, mycotoxin levels, and damage levels. 
     
     
         22 . The computer-readable storage medium of  claim 12 , wherein a first quality specification or a second quality requirement comprises 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 present or historical 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, country of origin, American Viticultural Area or origin, mountain grown, pollution-free grown, and carbon neutral grown. 
     
     
         23 . A method comprising:
 generating a first training set of data comprising remote sensor data corresponding to a location of a first crop product;   training a first machine-learned model configured to predict delineation of boundaries between fields of the first crop product using the first 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 use or application of conservation management practices for locations corresponding to the crop product type of the first crop product;   training a second machine-learned model configured to predict use or application of conservation management practices for locations for the first crop product based on remote sensor data corresponding to the first crop product using the second training set of data;   receiving a first request from a user of an online agricultural system identifying a first crop product type and first location of the first crop product, wherein the location of the first crop product is a state and county;   applying the first machine-learned model to the first location of the first crop product and the crop product type to predict one or more field boundaries of the first crop product location;   determining use or application of conservation management practices for the location of the first crop product of the first request by applying the second machine-learned model to remote sensor data corresponding to the predicted one or more field boundaries of the first crop product; and   modifying a first user interface presented to the user of the online agricultural system to include the predicted use or application of conservation management practices of first location of the first crop product.   
     
     
         24 . The method of  claim 23 , wherein the predicted use or application of conservation management practices is determined in real-time in response to detecting a change in remote sensor data corresponding to the first crop product. 
     
     
         25 . The method of  claim 23 , wherein the conservation management practices comprises one or more of: tillage, cover crops, and residue.