Abstract: A system for an agricultural harvester can include one or more processors and one or more non-transitory computer-readable media. The one or more non-transitory computer-readable media collectively store a machine-learned model configured to receive data associated with one or more harvest-related conditions for an agricultural harvester and process the data to determine a first harvest-related parameter associated with an infeed volume of harvest material for the agricultural harvester and instructions that, when executed by the one or more processors, configure the computing system to perform operations. The operations can include obtaining the data associated with one or more harvest-related conditions, inputting the data into the machine-learned model, and receiving a first value for the harvest-related parameter as a first output of the machine-learned model.

Abstract: A system for an agricultural harvester includes a chopper assembly configured to separate a harvested material into debris and stalk. A primary extractor is configured to remove debris from the harvester. A sensor system is configured to capture data associated with harvested material conditions downstream of the primary extractor. A computing system includes one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, configure the computing system to perform operations. The operations include obtaining the data associated with the associated harvested material conditions downstream of the primary extractor, determining a current foliage ratio based on the data, determining an error between the current foliage ratio to a desired foliage ratio, and generating a harvest-related parameter of the primary extractor based at least in part on the error.

Abstract: A system for an agricultural harvester includes one or more processors and one or more non-transitory computer-readable media that stores a machine-learned mass estimation model configured to receive data associated with one or more operation-related conditions for an agricultural harvester and process the data to determine an output indicative of a stalk mass for the agricultural harvester. The one or more non-transitory computer-readable media also stores instructions that, when executed by the one or more processors, configure the computing system to perform operations. The operations include obtaining the data associated with one or more operation-related conditions; inputting the data into the machine-learned mass estimation model; and receiving a value for the stalk mass as the output of the machine-learned mass estimation model for a defined time period.