Systems and methods for controlling the performance of a header severing system

Abstract

A system and method for controlling the performance of a severing system of an agricultural machine for cutting crop material. Information obtained at or around a cutterbar assembly of the severing system by a cutter sensor(s) can be used to identify an occurrence of a trigger condition. A machine learning model of a neural network can use at least a characteristic of the trigger condition, among other information regarding crop, environmental, and/or machine attributes and operator preferences, to identify one or more corrective actions. Once the corrective actions are automatically, semi-automatically, and/or manually implemented, the effectiveness of the corrective actions can be evaluated using feedback information, including feedback information representative of a cut quality being obtained by the cutterbar assembly. The effectiveness of the implemented corrective action(s) can be further be used by the neural network to train or retrain the machine learning model of the neural network.

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