A method and a system for determining an indicator of processing quality of an agricultural harvested material using a mobile device is disclosed. A computing unit analyzes image data of a prepared sample of harvested material containing grain components and non-grain components in an analytical routine to determine the indicator of the processing quality of the agricultural harvested material. Further, the computing unit uses a trained machine learning model in the analytical routine to perform at least one step of determining the indicator of the processing quality of the agricultural harvested material.

Corn header (4) for a forage harvester (1) having a plurality of mowing and infeed units (5), having a plurality of drum-like or chain-like mowing and infeed members (6), which are rotatable about a vertical axis and are drivable in a revolving manner, for severing the crop in a substantially horizontal direction, wherein the mowing and infeed members (6) arranged in the center between two sides (4a, 4b) of the corn header (4) are drivable in a revolving manner to convey crop out of a gap (12), forwards away from an infeed (9) of the forage harvester (1), wherein a central separating member (13) for separating the crop is positioned in a substantially vertical direction in the region of the gap (12) between the mowing and infeed members (6) arranged in the center of the corn header (4).

A method and system for identifying lengths of a particle in a flow of harvested material comprising particles in an agricultural harvester is disclosed. The agricultural harvester has at least one work assembly for harvesting a crop or for processing harvested material of the crop and can be adjusted using machine parameters. The harvested material is transported as a flow of harvested material through the agricultural harvester while the agricultural harvester is operating. The agricultural harvester has a camera that takes images of the flow of harvested material, with a computing unit of the agricultural harvester analyzing the images of the flow of harvested material in an analytical routine thereby finding particle lengths of particles of the flow of harvested material that are an excess length. The analytical routine is based on a machine learning method trained to find particles with the excess length, with the computing unit using the analytical routine to ascertain an amount of particles with the excess length.

A maize header comprising multiple mowing and infeed units (units 10), the first lateral mowing and infeed units (units 10b) are laterally next to central mowing and infeed units (units 10a), second lateral mowing and infeed units (units 10c) are laterally next to the first units (10b), and units (10d) are laterally next to the second units (10c), wherein, to transfer the maize header (2) between the working and a transport position, the units (10) are pivotable relative to one another about horizontal pivot axes (16) so that each of the units (10b) is pivotable about a first pivot axis (16a), each of the units (10c) is pivotable about a second pivot axis (16b), and each of the units (10d) is pivotable about a third pivot axis (16c), wherein a pivot angle (1) about the axes (16a) is 1644, a pivot angle (2) about the (16b) is 1804, and a pivot angle (3) about the axes (16c) is 1844.