Abstract: A computing system includes a processor and a memory having instructions stored thereon that, when executed by the one or more processors, cause the computing system to: receive yield data for a field and a spatial data set; process the yield data and the spatial data to generate a nitrogen loss spatial map layer; and generate a prescription. A computer-implemented method includes receiving yield data for a field and a spatial data set; processing the yield data and the spatial data to generate a nitrogen loss spatial map layer; and generating a digital agricultural prescription. A non-transitory computer readable medium includes program instructions that when executed by one or more processors, cause a computer to: receive yield data for a field and a spatial data set; process the yield data and the spatial data to generate a nitrogen loss spatial map layer; and generate a prescription.

Abstract: A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained deep learning model to generate predicted variety profile index values; and cause a visualization to be displayed.

Abstract: A method for soil data interpolation includes collecting first and second agricultural field point data values corresponding to a variable grid and generating interpolated point data values by analyzing the first and second agricultural field point data values. A computing system includes a processor and a memory storing instructions that, when executed by the processor, cause the computing system to collect first and second agricultural field point data values corresponding to a variable grid and generate interpolated point data values by analyzing the first and second agricultural field point data values. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to collect first and second agricultural field point data values corresponding to a variable grid and generate interpolated point data values by analyzing the first and second agricultural field point data values.

Abstract: A method for determining topographic attributes and environmental characteristics for environments includes determining respective topographic attributes and respective underlying environmental characteristics by processing raw machine data corresponding to environments encoded into respective hexagrid data structures; and storing the respective hexagrid data structures in memory. A computing system includes a processor; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to determine respective topographic attributes and respective underlying environmental characteristics by processing raw machine data corresponding to environments encoded into respective hexagrid data structures; and store the respective hexagrid data structures in memory.

Abstract: A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing: a trained machine learning model; and machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process agronomic input feature vectors to generate one or more predicted corn growth efficiency values; and provide the corn growth efficiency values as output. A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process labeled agronomic data with a machine learning model to generate one or more predicted corn growth efficiency values; and modify a parameter of the machine learning model.

Abstract: A method includes receiving raw machine data; determining topographic attributes and underlying environmental characteristics; encoding the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and storing the hexagrid data structures in a memory. A computing system includes a processor; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive raw machine data; determine topographic attributes and underlying environmental characteristics; encode the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and store the hexagrid data structures in the memory.

Abstract: A method for systematic application of agricultural products includes receiving a label, analyzing the label to generate procedures, receiving a product and field selection, generating a prescription including a map and the procedures, executing the procedures in the field, and displaying the map. A computing system includes a processor and a memory storing instructions that, when executed by the processor, cause the computing system to: receive a label, analyze the label to generate procedures, receive a product and field selection, generate a prescription including a map and the procedures, execute the procedures in the field, and display the map. A non-transitory computer readable medium stores program instructions that when executed, cause a computer to receive a label, analyze the label to generate procedures, receive a product and field selection, generate a prescription including a map and the procedures, execute the procedures in the field, and display the map.

Abstract: A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed. A computer-implemented method includes receiving a machine data set; processing the machine data set with a trained machine-learned model to generate predicted variety profile index values; and causing a visualization to be displayed. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by one or more processors, cause a computer to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed.

Abstract: A computing system includes a processor and a non-transitory, computer-readable medium including instructions that, when executed by the processor, causes the computing system to receive a machine data set; process the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmit the variety profile index values to a client computing device. A computer-implemented method includes receiving a machine data set; processing the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmitting the variety profile index values to a client computing device.

Abstract: A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed. A computer-implemented method includes receiving a machine data set; processing the machine data set with a trained machine-learned model to generate predicted variety profile index values; and causing a visualization to be displayed. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by one or more processors, cause a computer to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed.

Abstract: A method of improving agricultural treatment includes identifying a mineralogical feature based on a collected soil sample, generating a soil clay characterization based on the mineralogical feature, and generating an agricultural prescription. A system includes a processor and a memory storing instructions that, when executed by the processor, cause the system to identify a mineralogical feature based on a collected soil sample, generate a soil clay characterization based on the mineralogical feature, and generate an agricultural prescription. A non-transitory computer readable medium containing program instructions that, when executed, cause a computer to identify a mineralogical feature based on a soil sample, generate a soil clay characterization based on the mineralogical feature, and generate an agricultural prescription.

Abstract: Example machine learning (ML) methods and systems for characterizing corn growth efficiency (CGE), and generating field management recommendations based on CGE values are disclosed. An example computing system includes one or more processors, and storage media. The media storing an ML model trained using a training agronomic data set labeled with one or more known CGE values corresponding to one or more trial agricultural fields. The media further storing instructions that, when executed, cause the system to: obtain a production agronomic data set corresponding to a target agricultural field; determine one or more input feature vectors based on the production agronomic data set; process the one or more input feature vectors, with the ML model, to generate one or more predicted CGE values for one or more portions of the target agricultural field; and provide the one or more predicted CGE values as an output.

Abstract: A computing system includes a processor and a non-transitory, computer-readable medium including instructions that, when executed by the processor, causes the computing system to receive a machine data set; process the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmit the variety profile index values to a client computing device. A computer-implemented method includes receiving a machine data set; processing the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmitting the variety profile index values to a client computing device.

Abstract: A computing system includes a processor and a non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to access an initial machine data set; label the machine data set; process the labeled machine data set; and modify one or more parameters of the machine-learned model. A method includes accessing an initial machine data set; labeling the machine data set; processing the labeled machine data set; and modifying one or more parameters of the machine-learned model. A computing system for predicting a variety profile index includes a processor; and a non-transitory, computer-readable media including a trained machine-learned model; and instructions that, when executed by the one or more processors, cause the computing system to process a second machine data set to generate one or more predicted variety profile index values; and provide the one or more predicted variety profile index values.

Abstract: A computing system includes a processor and a non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to access an initial machine data set; label the machine data set; process the labeled machine data set; and modify one or more parameters of the machine-learned model. A method includes accessing an initial machine data set; labeling the machine data set; processing the labeled machine data set; and modifying one or more parameters of the machine-learned model. A computing system for predicting a variety profile index includes a processor; and a non-transitory, computer-readable media including a trained machine-learned model; and instructions that, when executed by the one or more processors, cause the computing system to process a second machine data set to generate one or more predicted variety profile index values; and provide the one or more predicted variety profile index values.

Abstract: A method includes receiving a machine data set; determining a respective soil strength measurement; processing the respective soil strength measurement to generate a recommendation including an executable agricultural prescription; and storing the executable agricultural prescription. A computing system includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; determine a respective soil strength measurement; process the respective soil strength measurement to generate a recommendation including an executable agricultural prescription; and store the executable agricultural prescription.

Abstract: A method includes receiving raw machine data; determining topographic attributes and underlying environmental characteristics; encoding the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and storing the hexagrid data structures in a memory. A computing system includes a processor; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive raw machine data; determine topographic attributes and underlying environmental characteristics; encode the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and store the hexagrid data structures in the memory.

Abstract: A method of improving sustainable agriculture includes collecting machine data, determining a sustainability measure, and providing a sustainability recommendation. A computing system includes a processor and a memory storing instructions that, when executed by the processor, cause the computing system to collect machine data; determine a sustainability measure; and provide a sustainability recommendation. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to collect machine data; determine a sustainability measure; and provide a sustainability recommendation.

Abstract: A computer-implemented method for providing an agricultural recommendation based on environmental matching includes collecting an underlying environmental characteristic corresponding to an agricultural environment, generating matches of the agricultural environment to a trials; and determining the recommendation based on the matches. A computing system includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to collect an environmental characteristic corresponding to an agricultural environment, generate matches of the agricultural environment to trials; and determine the recommendation based on the matches. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to collect an environmental characteristic corresponding to an agricultural environment, generate matches of the agricultural environment to trials; and determine the recommendation based on the matches.

Abstract: A method of improving agricultural treatment includes identifying a mineralogical feature based on a collected soil sample, generating a soil clay characterization based on the mineralogical feature, and generating an agricultural prescription. A system includes a processor and a memory storing instructions that, when executed by the processor, cause the system to identify a mineralogical feature based on a collected soil sample, generate a soil clay characterization based on the mineralogical feature, and generate an agricultural prescription. A non-transitory computer readable medium containing program instructions that, when executed, cause a computer to identify a mineralogical feature based on a soil sample, generate a soil clay characterization based on the mineralogical feature, and generate an agricultural prescription.