An impact sensor is mounted in a duct of a grain cleaning system above an upper sieve. The impact sensor has an upstream-facing impact-sensing surface with respect to a cleaning airstream, and is configured to transduce impact events and generate impact signals therefrom. An electronic control unit (ECU) is configured to generate control signals based upon a particle energy value that is determined from the impact signals. The control signals may serve to adjust various working units of a combine harvester including, by way of example, a cleaning fan and sieves.

A harvester includes a measuring device for detecting a grain number of a crop flow, in which a sensor of the measuring device detects via a measuring signal kernels impacting an impact surface of the measuring device, and a processing unit of the measuring device is arranged to detect and calculate the grain number by using the measuring signal, wherein the rising edges of the measuring signal are recorded and form a measurement for the grain number. Furthermore, the measuring device for detecting a grain number and method for detecting a grain number are also provided.

A sensor unit for use in the multiphase flow of a harvesting machine, wherein the sensor unit exhibits sensors for transmitting and/or receiving electromagnetic radiation. In addition, the sensor unit has at least one device for acquiring flow parameters of the multiphase flow. The measuring values of the sensor unit can advantageously be used for controlling the operating mode of the harvesting machine.

A harvesting machine capable of automatic adjustment, comprising a plurality of acoustic material flow sensors, a control system, a processor, and application software, wherein the plurality of acoustic material flow sensors are mounted internal to the harvesting machine at points of crop material flow and are capable of sensing an amount of crop material passing by them. The control system operates in a cause-and-affect mode for interactively enabling manual or automatic responses to Mass Material Distribution (MMD) information and equipment-related performance parameters. An interactive combine control method is also provided.

A crop quality sensor, comprising an illumination source, an imaging device, and a processor executing application software. The illumination source is shone onto a crop sample, and an image is taken with the imaging device of the illuminated crop sample. The software executing on the processor is used to analyze the image to identify the outlines of individual kernels and to identify which of those outlines contain a specular highlight, indicative that the kernel is whole and unbroken, while the absence of such a specular highlight is indicative of a broken kernel.

A method and system for controlling the quality of harvested grains include capturing, by one or more image sensors, one or more images of material at a sampling location within a grain elevator of the combine harvester. The captured images are defined by a set of image pixels represented by image data and having a classification feature indicative of grain or non-grain material. One or more controllers receive the image data associated with the one or more images captured by the image sensor(s) and select a sample image defined by a subset of image pixels of the set of image pixels. The controller(s) apply a convolutional neural network (CNN) algorithm to the image data of the subset of image pixels of the selected sample image to determine the classification feature. The controller(s) analyze the determined classification feature to adjust an operational parameter of the combine harvester.

A material independent mass flow sensor is used to generate signals that can be used to calculate mass flow of grain harvested by a combine. A method for determining a mass of material includes the steps of receiving data from a three-measurement transducer and determining an angular center of mass location of an object based on the data from the three-measurement transducer. A coefficient of friction of the object is determined. A velocity of the object is determined. A mass of the object is determined. The mass of the object can be determined based on the angular center of mass location of the object, the coefficient of friction of the object, and the velocity of the object.

A system and a method are provided for controlling a combine harvester. The method includes the steps of: receiving grain flow sensor signals from a plurality of grain flow sensors; based on the received grain flow sensor signals, determining a current load on a straw walker section; and based on the current load, adjusting an aggressiveness setting of a threshing and separation section of the combine harvester. The grain flow sensors are provided underneath and adjacent to a crop transfer surface of the straw walker section of the combine harvester and are distributed over a length of the straw walker section.

One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a material dynamics characteristic value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that predicts a predictive material dynamics characteristic value at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the material dynamics characteristic value detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

A yield model generates a yield map for an agricultural field. A measurement system generates measured indicators that are a measurement or quantification of crop yield in the agricultural field. An observation system generates observed indicators that are spatial agricultural datasets describing observed characteristics of the agricultural field. To generate the yield map, the yield model generates a field array representing the agricultural field. The yield model generates an input array and a yield array by mapping the observed indicators and measured indicators to cells of the field array, respectively. The yield model determines a yield value for each cell of the yield array not including a mapped indicator using information included in the corresponding cells of the input array. The yield model generates a yield map using the determined yield values and the yield values in the yield array.