The Climate Corporation

Shilpa Sood

Xiao Yang

Herbert Ssegane

Jason Kendrick Bull

Bruce J Schnicker

Tonya S Ehlmann

Timothy Reich

Yao Xie

Morrison Jacobs

Allan Trapp

Matthew Sorge

Nikisha Shah

Susan Andrea Macisaac

Dongming Jiang

Xuefei Wang

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.

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

An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

Leveraging feature engineering to boost placement predictability for seed product selection and recommendation by field

Growing community of inventors

Chesterfield, MO, United States of America

Shilpa Sood