Crop yield estimation using agronomic neural network

Systems and method for computing yield values through a neural network from a plurality of different data inputs are disclosed. In an embodiment, a server computer system receives a particular dataset relating to one or more agricultural fields wherein the particular data set comprises particular cr...

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Bibliographische Detailangaben
Hauptverfasser:	Andrejko, Erik, Guan, Wei
Format:	Patent
Sprache:	eng
Schlagworte:	AGRICULTURE ANIMAL HUSBANDRY CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES FISHING FORESTRY HUMAN NECESSITIES HUNTING PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES ORIMPLEMENTS, IN GENERAL PHYSICS SOIL WORKING IN AGRICULTURE OR FORESTRY SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TRAPPING
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Beschreibung
Zusammenfassung:	Systems and method for computing yield values through a neural network from a plurality of different data inputs are disclosed. In an embodiment, a server computer system receives a particular dataset relating to one or more agricultural fields wherein the particular data set comprises particular crop identification data, particular environmental data, and particular management practice data. Using a first neural network, the server computer system computes a crop identification effect on crop yield from the particular crop identification data. Using a second neural network, the server computer system computes an environmental effect on crop yield from the particular environmental data. Using a third neural network, the server computer system computes a management practice effect on crop yield from the management practice data. Using a master neural network, the server computer system computes one or more predicted yield values from the crop identification effect on crop yield, the environmental effect on crop yield, and the management practice effect on crop yield.