Digital rights management of machine learning models

Abstract

Techniques for training an agricultural inference machine learning model to generate valid agricultural inferences of agricultural conditions based on ground truth sensor data that falls within a plurality of ground truth sensor value ranges associated with a particular agricultural area, and to generate invalid or ambiguous agricultural inferences of agricultural conditions based on ground truth sensor data that falls outside of the plurality of ground truth sensor value ranges associated with a particular agricultural area. The agricultural inference machine learning model is trained, based on ground truth sensor data for the particular agricultural area, to determine if the subsequently received ground truth sensor data falls within or outside of that plurality of ground truth sensor value ranges that correspond to the particular agricultural area.

CPC Classifications