A control system for a harvester that harvests crop stalks each having a bottom portion and a top portion. The harvester includes a topper that cuts the stalks between the top and bottom portions and a base cutter that cuts the stalks near a ground surface. The control system includes a sensor that senses a first height between the top of the bottom portion and the ground surface, and senses a second height between the bottom of the bottom portion and the ground surface. The controller receives signals representing the sensed first and second heights from the sensor, determines average first and second heights for the stalks over a set time, sends a first signal to the topper to cause movement of the topper to the average first height, and sends a second signal to the base cutter to cause movement of the base cutter to the average second height.

An accumulator contents detection system for a harvester includes an accumulator, a plurality of metering rollers, and at least one sensor. The accumulator accumulates crop material. The plurality of metering rollers receives crop material from the accumulator. The plurality of metering rollers includes a drive metering roller. The at least one sensor detects a load transmitted to the drive metering roller.

A cotton harvester estimates the mass of cotton as it is harvested using sensor devices and compares the mass of each module against the estimated mass of the module as determined by the sensors so that a calibration factor may be determined and actively updated for more accurate crop yield determination. The mass flow for a specific module is accumulated and processed during harvesting using a base calibration factor and the module is weighed and compared against the expected mass using the base calibration factor to develop a candidate updated calibration factor. The base calibration factor is selectively replaced by the candidate updated calibration factor for processing a subsequent module based on machine feedback information relating to the operation of the harvester. Harvested crop data determined using the calibration factor is used to generate highly accurate yield maps.

A method for determining crop yield may include receiving yield data, associated respectively with a plurality of harvester machines. The method may further include determining a primary harvester machine, where the primary harvester machine is associated with a largest total harvested area of the plurality of total harvested areas. The method may also include determining a plurality of adjacent harvested areas associated, respectively, with each of the plurality of harvester machines, other than the primary harvester machine, and determining a secondary harvester machine, where the secondary harvester machine is associated with a largest adjacent harvested area of the plurality of adjacent harvested areas. The method may include adjusting a yield measurement associated with the secondary harvester machine using yield measurements associated with the primary harvester machine and generating calibrated yield data including the adjusted yield measurements.

A dynamic event detection system detects dynamic events, based on a sensor signal, on a mobile harvester. A dynamic event correction system identifies a correction magnitude, corresponding to the detected dynamic event, and a correction timing. The dynamic event correction system applies a correction, using the correction magnitude and correction timing, to a performance metric value generated from a performance metric sensor.

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.

A method of measuring a harvested crop includes measuring a first attribute of a first electric field in a first volume containing crop material, measuring a second attribute of a second electric field in a second volume containing crop material, and determining at least two different properties of the crop material based at least in part on the first attribute and the second attribute.

An agricultural machine capable of sensing a harvested crop load includes a mower or mower conditioner implement, which may be referred to as a crop cutting implement, and a retractable linear device such as a spring or linear actuator. The crop cutting implement includes a flexible drive assembly with an endless loop, such as a belt, that is driven and supported by rollers. The retractable linear device resists movement of a first roller relative to a second roller. With this arrangement various characteristics of the agricultural machine, all of which are proxies for the position of the first roller relative to the second roller, may be measured by a crop load sensor to determine the quantity or load of harvested crop being processed by the flexible drive assembly of the agricultural machine.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

A priori geo-referenced 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 geo-referenced 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.