LEVERAGING FEATURE ENGINEERING TO BOOST PLACEMENT PREDICTABILITY FOR SEED PRODUCT SELECTION AND RECOMMENDATION BY FIELD
Abstract:
An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of one or more products grown in a given field and continuous data indicative of multiple raw field features and specific to the given field. The method also includes transforming the raw field features into distinct feature classes and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between 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.
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