Invention Grant
- Patent Title: Using machine learning-based seed harvest moisture predictions to improve a computer-assisted agricultural farm operation
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Application No.: US16661860Application Date: 2019-10-23
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Publication No.: US11017306B2Publication Date: 2021-05-25
- Inventor: Shilpa Sood , Matthew Sorge , Nikisha Shah , Timothy Reich , Herbert Ssegane , Jason Kendrick Bull , Tonya S. Ehlmann , Morrison Jacobs , Susan Andrea Macisaac , Bruce J. Schnicker , Yao Xie , Allan Trapp , Xiao Yang
- Applicant: THE CLIMATE CORPORATION
- Applicant Address: US CA San Francisco
- Assignee: THE CLIMATE CORPORATION
- Current Assignee: THE CLIMATE CORPORATION
- Current Assignee Address: US CA San Francisco
- Agency: Hickman Becker Bingham Ledesma LLP
- Agent Christine E. Orich
- Main IPC: G06N5/04
- IPC: G06N5/04 ; A01B79/00 ; G06N20/00

Abstract:
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.
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