Incorporating unstructured data into machine learning-based phenotyping
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United States of America
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Mar 25, 2025
Grant Date -
Nov 9, 2023
app pub date -
May 5, 2022
filing date -
May 5, 2022
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Abstract
Implementations are described herein for incorporating unstructured data into machine learning-based phenotyping. In various implementations, natural language textual snippet(s) may be obtained. Each natural language textual snippet may describe environmental or managerial features of an agricultural plot that exist during a crop cycle. A sequence-to-sequence machine learning model may be used to encode the natural language snippet(s) into embedding(s) in embedding space. The embedding(s) may semantically represent the environmental or managerial features of the agricultural plot. Using one or more phenotypic machine learning models, phenotypic prediction(s) may be generated about the agricultural plot based on the one or more semantic embeddings and additional structured data about the agricultural plot. Output may be provided at one or more computing devices that is based on one or more of the phenotypic predictions.
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