Claas Selbstfahrende Erntemaschinen Gmbh

Claas Tractor Sas

Christoph Heitmann

Joachim Baumgarten

Andreas Wilken

Sebastian Neu

Christoph Bussmann

Henner Voecking

Christopher Vieregge

Boris Kettelhoit

Bastian Bormann

Stefan Teroerde

Ingo Bönig

Frédéric Fischer

Jochen Huster

Dennis Neitemeier

Markus Brune

Henner Vöcking

Stefan Terörde

Bernd Holtmann

Thilo Krause

Norbert Strieker

Jan Carsten Wieckhorst

Christian Ehlert

Sven Carsten Belau

Georg Eggenhaus

Bastian Kriebel

Carsten Grove

Arthur Berger

Andreas Wielenberg

Stefan Terorde

Stefan Schiewer

Alexander Kirchbeck

Stefan Look

Andreas Diekamp

Mathieu Köhler

Thorsten Stiller

Bjoern Stremlau

Felix Herter

Joern Brinkmann

Scott Wellensiek

Klaus Schaefer

Björn Stremlau

Jan Meyer Zu Hörste

Jens Heydekorn

Benedikt Feldotto

Axel Schroeder

Jan Furmaniak

Axel Schröder

René Middelberg

Pierre Noyer

Christian Beulke

Rainer Bellmann

Jöm Brinkmann

Stefan Birkhofer

Klaus Schäfer

Patrick Guillaume

Sofia Silvestre Nunez

Jörn Brinkmann

Henning Reßmeyer

Dr Joachim Baumgarten

Scott Wllensiek

Johannes Kersten

Ingo Bönig Dr

Henner Vocking

Markus Neuhauser

Peter Kajtar

Andre Dammann

Gregor Wehner

Sebastian Harsch

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 detection arrangement for detecting a wear status of a chopping knife arrangement of a chopping device provided for processing a product flow, wherein the chopping device has a revolving chopping drum receiving the chopping knife arrangement and at least one shear bar which cooperates with the chopping knives, with a sensor arrangement which has a magnetic exciter arrangement and a flux conducting device magnetically coupled thereto. The sensor arrangement provides a pole arrangement which forms at least one magnetic pole with a pole surface for conducting magnetic flux, wherein at least a portion of the chopping knife passes the pole arrangement during a rotation of the chopping drum. A voltage induced when a chopping knife arrangement passes the sensor arrangement forms the measured magnetic value, which is used by the evaluation unit to determine the state of wear of the chopping knife arrangement.

Cutting sharpness detection device

A combine harvester for carrying out an agricultural harvesting process has a plurality of working units and a driver assistance system for controlling at least some of the working units. The combine harvester accommodates as working units a chaff cutter for comminuting harvested produce and a produce distributing arrangement in the rear area of the combine harvester downstream of the chaff cutter for distributing harvested produce on the field soil in adjustable throw directions. The respective throw direction comprises vector components of a horizontal throw direction and a vertical throw direction. The driver assistance system is adapted to optimize the control of the produce distributing arrangement by at least one of the substrategies including 'throw direction correction' and/or 'inclination-dependent produce distribution' and/or &#x201c;produce distribution in longitudinal direction&#x201d; and/or &#x201c;produce distribution in transverse direction&#x201d; with respect to at least one optimization criterion.

Combine harvester with produce distribution substrategies

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

An agricultural work machine for performing an agricultural work process is disclosed. The agricultural work machine includes working units and a driver assistance system for controlling the working units to achieve one or more quality criteria. The driver assistance system may set parameters to control the working units in order to satisfy the criteria. Further, the driver assistance system includes a graphical user interface through which an operator may change the setting of one of the quality criteria. Responsive to the change, the driver assistance system may determine the expected effects on other quality criteria. In addition, the driver assistance system may visually highlight the expected effects on the graphical user interface.

Forage harvester and method for operating a forage harvester

A forage harvester comprising an edge sharpness detection device for detecting a degree of edge sharpness of a cutting mechanism. The cutting mechanism comminutes a stream of harvested material, with a material inflow area being defined where the cutting blades interact with the shear bar to comminute the harvested material. The edge sharpness detection device excites one or more magnetic circuits, with the respective magnetic circuit being closed by the respective cutting blade during rotation of the cutter drum once one of the cutting blades passes the magnetic assembly. The edge sharpness detection device detects the magnetic flux in the respective magnetic circuit and, based on a detected change, determines a degree of edge sharpness of the respective cutting blade. Further, the magnetic flux of the magnetic circuit may be guided lengthwise in the cutting blade at least along a longitudinal section of the respective cutting blade.

Forage harvester with a magnetic assebmly

A combine harvester having a chopper configured for chopping crop, which comprises a cutting cylinder fitted with chopper knives, a knife carrier fitted with counter knives, and a rasp bar arrangement. The counter knives and/or the rasp bar arrangement can be moved into a partial stream passing by the chopper. The harvester also has a crop spreading device configured for spreading the chopped-material stream emerging from the combine harvester on the ground in the rear region of the combine harvester, and a sensor unit configured for generating sensor data which represent at least one parameter influencing the spreading quality of the chopped-material stream emerging from the combine harvester. There is a control unit that adapts the position of the rasp bar arrangement and/or the counter knives depending on the spreading quality.

Combine harvester

This disclosure relates to a method and device for the operation of a combine harvester with multiple work elements for harvesting a crop via a driver assistance system. In one example implementation, a method for operating such a combine harvester includes determining separately a harvest setting for the crop and a machine configuration of the combine harvester, preselecting a process control strategy among a plurality of process control strategies based on the harvest setting and machine configuration wherein each of the plurality of process control strategies is aimed at fulfillment of at least one harvesting quality criterion stored in the memory of the combine harvester, and automatically determining at least one machine parameter for the work elements of the combine harvester according to the preselected process control strategy and controlling the work elements corresponding to the at least one machine parameter.

Method and apparatus for operating a combine harvester

A combine harvester which regulates spreading of crop flow on the ground, with the crop flow passing through a shredding and/or a spreading device in the rear region of the combine harvester, is disclosed. The combine harvester includes a driver assistance system, which includes a computing unit and a display unit. The computing unit processes information generated by the machine's internal sensor systems, external information and information storable in the computing unit. The driver assistance system stores selectable spreading strategies to regulate the spreading of the crop flow exiting the combine harvester and one or more of the partial strategies assigned to the respective spreading strategy. The driver assistance system uses the selectable spreading strategies and the partial strategies in order to regulate the crop flow spread on the ground.

Combine harvester having a driver assistance system

A method for determining calibration data for a grain-loss sensor on a combine harvester includes measuring grain-loss quantities, which were left behind by the combine harvester during a harvesting operation, and measuring sensor grain-loss values by the grain-loss sensor during the harvesting operation. Reference marks are assigned to the measured grain-loss quantities and the measured sensor grain-loss values and the calibration data for the grain-loss sensor are determined on the basis of a reconciliation of the measured grain-loss quantities with the measured sensor grain-loss values according to the reference marks.

Method for determining calibration data for grain-loss sensor

A combine harvester includes a threshing unit for threshing picked-up crop to obtain grain, a driver assistance system, with a memory for storing data, for controlling the threshing unit, and a computing unit for processing at least the data stored in the memory. A sensor system ascertains at least a portion of the current harvesting-process state. A sensor configuration assigned to the sensor system is defined by the type and scope of operational sensors is stored or can be stored in the memory. A functional system model for at least a portion of the combine harvester is stored in the memory. The computing unit is designed to carry out an autonomous determination of a threshing-unit parameter based on the system model, and defines the system model forming a basis of the control of the threshing unit depending on the sensor configuration stored in the memory.

An agricultural work machine for processing an agricultural work process, having numerous working units (-) and having a driver assistance system () for controlling the working units (-) according to at least one user-side specifiable work process strategy (A), which is aimed at fulfilling at least one quality criterion (Q), wherein the driver assistance system () comprises a memory () for storing data, and a computing device () for processing the data stored in the memory (), wherein the driver assistance system () has a graphical user interface (), via which at least a portion of the work process strategy (A) can be specified by a user. It is proposed that competing quality criteria (Q) that are weighted in relation to one another in accordance with a weighting variable (G) are entered into the work process strategy (A), and that the weighting variable (G) is visualized and can be specified by a user via a virtual operating element (-) of the graphical user interface ().

Agricultural work machine

Growing community of inventors

Warendorf, Germany

Christoph Heitmann