A method for controlling one or more operating parameters of a forage harvester includes picking up harvested crop from a field by means of a harvesting attachment, processing the harvested crop by means of a chopping device and/or a post-processing device, and ejecting the processed harvested crop onto a loading container. An image of the processed ground-borne harvested crop is recorded by a camera, and an operating parameter of the forage harvester is set or adjusted with a control unit using the image.

A method for true-to-sample measurement by a mobile ingredient analysis system having a housing with a window, an interface for an external reference unit, a display and operating unit, a light source, an optical spectrometer, a camera, an internal reference unit, and an electronic control unit. The method includes: selecting a calibration product suitable for a sample to be examined; performing a plausibility check of the calibration product, an incorrect selection being signaled and an alternative calibration product being selected; outputting measurement conditions comprising the measurement point to be selected and measurement duration for the selected calibration product; capturing measured values of the sample by the spectrometer under the measurement conditions and with simultaneous monitoring of the measurement conditions; processing the captured measured values by means of an electronic control unit, each measured value captured while the measurement conditions were met being declared valid; outputting the measured values deemed valid.

At least one sensor carried by a mobile machine senses a sensed forage crop attribute value independent of plant population for an individual forage plant. A processing unit derives a derived forage crop attribute value based on the sensed forage crop attribute value. The derived forage crop attribute value is used to determine a field condition.

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 priori georeferenced vegetative index data is obtained for a worksite, along with field data that is collected by a sensor on a work machine that is performing an operation at the worksite. A predictive model is generated, while the machine is performing the operation, based on the georeferenced vegetative index data and the field data. A model quality metric is generated for the predictive model and is used to determine whether the predictive model is a qualified predicative model. If so, a control system controls a subsystem of the work machine, using the qualified predictive model, and a position of the work machine, to perform the operation.

A sugarcane harvester for harvesting sugarcane including a cutter configured to cut sugarcane into a sugarcane mat. An extractor is operatively connected to the cutter, and includes a fan housing having an inlet configured to receive the sugarcane mat and an outlet configured to discharge crop debris. A fan is located in the fan housing to move the sugarcane mat through the fan housing and includes a motor, blades rotatably coupled to the motor, and a blade angle of incidence mechanism operatively connected to the blades, wherein the blade angle of incidence mechanism adjusts the angle of incidence of the blades with respect to the motor. Sensors identify an air flow through the fan housing or a load placed on the motor during movement of the sugarcane mat through the fan housing. The angle of incidence of the blades is adjusted based on one or both of the air flow through the fan housing or the load placed on the motor.

An agricultural machine configured to be operated by an operator and including a drive mechanism having a drive shaft and a gear assembly for driving an agricultural implement. The agricultural machine includes a slip clutch that has an outer sleeve operatively coupled to the gear assembly, an inner sleeve positioned within the outer sleeve and coupled to and rotatable with the drive shaft, a plurality of torque-transfer members positioned between the outer sleeve and the inner sleeve and configured to selectively couple the inner sleeve to the outer sleeve, and a position sensor in communication with one of the plurality of torque-transfer members and configured to output a signal in response to radial displacement of the one torque-transfer member being detected. The agricultural machine includes a control unit in communication with the position sensor and configured to alter an operating parameter of the machine in response to receiving the signal.

A residue vision system includes an image acquisition device configured for mounting to a harvesting machine. The image acquisition device is configured to acquire, during operation of the harvesting machine, information regarding a first pass in a field. The first pass is a harvested area of the field. During the operation, the first pass is laterally offset from a second pass in the field. An electronic control unit is in communication with the image acquisition device and with a residue distribution system. The residue distribution system is configured to be coupled to the harvesting machine to distribute residue onto the second pass and onto the first pass. The electronic control unit is configured to analyze the information acquired by the image acquisition device and to adjust a distribution of residue onto the first pass while the harvesting machine is harvesting the second pass.

In one aspect, a system for monitoring crop yield for an agricultural harvester includes a material processing system configured to receive a flow of harvested materials, a first sensor configured to generate data indicative of a volume of the flow of harvested materials being directed through the material processing system, and a second sensor configured to generate data indicative of a density of the flow of harvested materials being directed through the material processing system. In addition, the system includes a computing system communicatively coupled to the first and second sensors, with the computing system being configured to determine a mass flow rate of the flow of harvested materials through the material processing system based at least in part on the data received from the first and second sensors.

Provided is a non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to obtain a first signal based on an input image using a trained machine learning model, the input image being an image of a plant cut by a blade, and the first signal indicating a wear level of the blade, determine whether a level of the first signal is greater than or equal to a threshold, generate a second signal in response to determining the level of the first signal is greater than or equal to the threshold, and output the second signal.