Claas Selbstfahrende Erntemaschinen Gmbh

Claas Saulgau Gmbh

Ingo Bönig

Christoph Heitmann

Sven Carsten Belau

Frédéric Fischer

Bastian Kriebel

Carsten Grove

Alexander Kirchbeck

Andreas Haffert

Stefan Schiewer

Stefan Look

Ludger Laumeier

Felix Herter

Jan Furmaniak

Stefan Birkhofer

Björn Stremlau

Jan Meyer Zu Hörste

Sebastian Harsch

Gregor Wehner

Andre Dammann

Peter Kajtar

Christian Cord-Kruse

Rainer TÖLLE

Tarek Helal

Marcel Boppenmaier

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.

System and method for determining an indicator of processing quality of an agricultural harvested material

A forage harvester. The forage harvester has a work assembly for harvesting a crop and for processing harvested material of the crop, which includes grain components and non-grain components. In operation, the harvested material is transported in a harvested material flow along a harvested material transport path through the harvesting machine. The forage harvester also has a control assembly that includes an optical measuring system arranged on the harvested material transport path. The optical measuring system has a camera for recording image data of the harvested material of the harvested material flow. The control assembly, using an image recognition routine, determines image regions assigned to a non-grain component in the image data, determines geometric properties of the assigned non-grain components based on the image regions, and determines an indicator of a structural percentage of the harvested material from the geometric properties.

Forage harvester

A forage harvester with at least one work assembly is disclosed. The forage harvester has a corn cracker to process grain components and a driver assistance system. The driver assistance system controls the corn cracker by adjusting the machine parameters of the corn cracker. In particular, the driver assistance system has an optimization model which includes a multidimensional characteristic map that represents a relationship between a processing quality of the grain components and at least three parameters that comprise an input parameter representing a current harvesting process state and at least one machine parameter of the corn cracker as an output parameter. Thus, the driver assistance system determines the output parameter in the control routine during the harvesting process based on the varying input parameter from the optimization model and adjusts it in the corn cracker to achieve a uniform given processing quality of the grain components during the harvesting process.

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 and that the computing unit adjusts at least one machine parameter of the forage harvester based on the indicator of processing quality.

A self-propelled forage harvester is disclosed. The forage harvester includes a feed device, a chopping device comprising a cutterhead equipped with cutting blades and a shear bar for comminuting harvested material, a drive, and a driver assistance system for controlling at least the chopping device. The driver assistance system includes a memory for saving data and a computing device for processing the data saved in the memory, wherein the chopping device and the driver assistance system in combination form an automatic chopping system in that the computing device continuously determines a compaction of the comminuted harvested material using harvested material parameters during a harvesting process in order to autonomously ascertain and specify a cutting length to be adapted for maintaining nearly constant compactability.

Self-propelled forage harvester with automatic chopping system

Growing community of inventors

Gütersloh, Germany

Ingo Bönig