A method is provided for operating a system consisting of an agricultural working vehicle, at least one working tool arranged thereon, and a controller assigned to the working vehicle. The method includes detecting, by at least one sensor assembly arranged at least on the working tool and having at least two sensors, two different physical variables. The method further includes storing, by a memory, information characterizing the working tool, continuously storing, by the memory, operating data of at least the working tool, and communicating wirelessly via Bluetooth network with the controller by the at least one sensor assembly via a transmitter. The controller is brought into a transmission range of the sensor assembly to activate the communication between them and the operating data temporarily stored in the memory is transmitted to the controller.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, at least, detecting an optical sight to align with an associated agricultural object, tracking the agricultural objects relative to the optical sight, predicting a parameter to track in association with agricultural object, and activating an emitter to apply an action based on the parameter.

The present teachings relate to a computerimplemented method comprising: receiving, at a processing means, a crop data indicative of the type of a crop; receiving, at the processing means, a region data indicative of at least one harmful organism that may be present at or around the location of the crop; selecting from a database, using the processing means, a sensor arrangement suitable for selectively genotyping at least one relevant harmful organism; wherein the at least one relevant harmful organism is among the at least one harmful organism, and wherein the selection is performed in response to the crop data and the region data. The present teachings also relate to: an electronic device comprising a processing means configured to execute the method steps disclosed; and, a computer software product.

A system (1) for measuring and interpreting a force, comprises at least one working implement, for acting on an agricultural field and at least one force sensor, for measuring a force of the working implement. Further, a data interpretation unit calculates an interpretation of the measured force; wherein the data interpretation unit comprises a machine learning unit that calculates the interpretation of the measured force. Also, a system for controlling agricultural operations comprises at least one agricultural working means for working on an agricultural field and at least one first imaging device located at the agricultural working means for acquiring images of an environment of the agricultural working means.

A system including a sensor configured to detect an orientation of the vehicle, a vehicle ECU configured to communicate with the sensor and with a work machine, a work machine ECU configured to communicate with the vehicle and to control performance of a work function of the work machine, and wherein, the sensor is configured to communicate a detected orientation of the vehicle to the vehicle ECU, the vehicle ECU is configured to transmit a signal to the work machine ECU if the detected orientation is outside of target parameters corresponding to an orientation of the vehicle, and the signal is configured to notify the work machine ECU of the detected orientation and/or modify operation of a work function performed by the work machine.

A computing system includes a processor and a non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to access an initial machine data set; label the machine data set; process the labeled machine data set; and modify one or more parameters of the machine-learned model. A method includes accessing an initial machine data set; labeling the machine data set; processing the labeled machine data set; and modifying one or more parameters of the machine-learned model. A computing system for predicting a variety profile index includes a processor; and a non-transitory, computer-readable media including a trained machine-learned model; and instructions that, when executed by the one or more processors, cause the computing system to process a second machine data set to generate one or more predicted variety profile index values; and provide the one or more predicted variety profile index values.

A system and computer-implemented method for assessing an amount of ash includes an agricultural implement and an ash assessment system. The implement includes a crop processing portion configured to move a cut organic material in a field. The ash assessment system includes an ash sensor configured to determine a measured ash contamination value, a GNSS receiver configured to determine a geolocation, and a processor programmed to perform an ash assessment process. The process includes receiving the measured ash contamination value from the ash sensor and receiving the geolocation data from the GNSS receiver. The geolocation data corresponds to a geolocation of the measured ash contamination value. The process also includes correlating the measured ash contamination value and the geolocation data to one another and storing the measured ash contamination value and the geolocation data in a memory device that is coupled in communication with said processor.

Methods, apparatus, systems and articles of manufacture are disclosed for milestone prediction of fuel and chemical usage. An example apparatus includes one or more memories comprising computer readable instructions; one or more processors to execute the computer readable instructions to determine a current amount of fuel required without halt and a current fuel consumption rate for a machine during a harvesting event in a field based on a first amount of fuel required without halt, a first fuel consumption rate, and real time information from sensors of the machine, and determine a real time amount of fuel required based on the current amount of fuel required without halt, the current fuel consumption rate, and a halt time for the machine during the harvesting event, the one or more processors to use the real time amount of fuel required to schedule fuel delivery for the machine.

Systems and methods for controlling formation of a windrow in response to a single control input are disclosed. The single control input represents a desired windrow width. The single control input is used to determine positions of components of a cutter implement, such as side shields and a swath flap, that are used to form a windrow having the desired windrow width represented or that is otherwise associated with the single control input.

A method for shadow and cloud masking for remote sensing images of an agricultural field using multi-layer perceptrons includes electronically receiving an observed image, performing using at least one processor an image segmentation of the observed image to divide the observed image into a plurality of image segments or superpixels, extracting features for each of the image segments using the at least one processor, and determining by a cloud mask generation module executing on the at least one processor a classification for each of the image segments using the features extracted for each of the image segments, wherein the cloud mask generation module applies a classification model including an ensemble of multilayer perceptrons to generate a cloud mask for the observed image such that each pixel within the observed image has a corresponding classification.