Using machine learning-based seed harvest moisture predictions to improve a computer-assisted agricultural farm operation

Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshol...

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Bibliographische Detailangaben
Hauptverfasser: Trapp, Allan, Yang, Xiao, Ehlmann, Tonya S, Schnicker, Bruce J, Sorge, Matthew, Macisaac, Susan Andrea, Reich, Timothy, Sood, Shilpa, Shah, Nikisha, Bull, Jason Kendrick, Jacobs, Morrison, Ssegane, Herbert, Xie, Yao
Format: Patent
Sprache:eng
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Zusammenfassung:Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.