In order to achieve a more effective application of a crop efficiency product, a computer-implemented method is provided for applying a crop efficiency product to at least one crop in a field. The method comprises the steps of collecting remotely-sensed data of the field before an application of the crop efficiency product in the field, determining, based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field, generating, for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product, deciding, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.

Systems and methods automate the design and execution of randomized experiments. Portions of a field are planted using an agricultural vehicle configured to randomly vary planting parameters when planting a portion of the field. A resulting crop outcome across each portion or sub-portion of the field is observed. A training set of data is generated that includes the varied planting parameters and the associated crop outcomes for each portion of the field. A machine-learned model is trained using the training set of data and is configured to predict a crop outcome for a portion of the field based on historical and forecast conditions and a set of planting parameters applied to a portion of the field. For subsequent iterations, for a target portion of the field, the machine-learned model can be applied to identify a set of planting parameters for planting the target portion of the field to optimize a desired crop outcome.

A system for an agricultural sprayer includes a boom assembly operably coupled with a chassis. A steering system is operably coupled with the chassis and includes a steering sensor. The system also includes one or more imaging devices and one or more nozzle assemblies. A computing system is operably coupled with the one or more imaging devices and the one or more nozzle assemblies. The computing system is configured to receive data related to a first imaged portion of an agricultural field from the one or more imaging devices; identify a target within the first imaged portion of the agricultural field; receive data related to an inputted steering command from the steering system; and determine a target offset of the target relative to the sprayer path and a boom offset of the assembly relative to the sprayer path.

A view-based method for controlling the driving of agricultural machinery includes collecting ground information from images; identifying a target operation area according to the ground image information; determining a navigation route for the agricultural machinery within the target operation area; and determining whether the navigation route is reliable; detecting manual driving signal of the user and allowing manual driving of the agricultural machinery according to the manual driving signal if the navigation route is not reliable; and determining driving adjustment parameters for the navigation route and current driving attitude if the navigation route is found reliable. A system for driving agricultural machinery, a device applying the method, and a non-volatile storage medium are also disclosed.

A method including entering, by machine, a first field defined by first field boundaries; and automatically associating the machine with a first wireless network defined by the first field boundaries, the first wireless network comprising a secured data communications network.

A fault database includes a fault identifier, a signature or pattern that indicates the presence of the fault, and a set of mitigation control steps. The fault database is intermittently updated and downloaded to an agricultural machine. A fault identification system on the agricultural machine scans data logs that are generated by a log generation system on the agricultural machine and compares information in the data logs to the signature or pattern in the fault database to determine whether any of the faults in the fault database are present on the agricultural machine. If a fault in the fault database is present, a mitigation control step is identified to mitigate the fault, and a control signal is generated on the agricultural machine to implement the mitigation control step.

In accordance with an example embodiment, a method for directing a work machine to one or more worksites from a selection of candidate worksites is disclosed. The method includes receiving obscurant data related to a forecast availability of obscurants at one or more worksites; receiving environmental data related to the suppression, creation, transportation, or direction of obscurants; and receiving operational data related to machine components and the ability of the machine components to generate obscurants or have performance degraded by obscurants at the one or more worksites. Determining an obscurant metric for each of the one or more worksites based on the obscurant data, the environmental data, and the operational data; and directing the work machine to the one or more worksites based on the obscurant metric.

An automatic guidance system is adapted to be mounted on a work vehicle such as a farm tractor for assisting an operator steer the vehicle on a desired track relative to a furrow. The system includes sensors for transmitting and receiving ultrasonic ranging signals. The sensors are ultrasound transducers mountable on ends of a planter drawn by the vehicle for directing ranging signals downwardly toward field adjacent of a furrow such that the ranging signals strike the field or furrow and are reflected back into the respective sensor. Guidance logic stored in a memory of a controller is executed by a processor to determine tractor headway direction and headland turning directions representative of desired tractor headway and headland turning directions, and a human interface device generates guidance images viewable by an operator for steering the tractor relative to furrows in the field and in the headland.

Disclosed is an automatic row-guiding method for maize combine harvester based on the situation of missing plants, comprising: S1, guiding calculation of missing plants according to a traveling speed of a harvester and output values of left and right detecting sensors; and S2, performing guiding calculation of missing plants if there is a situation of missing plant, obtaining a first target turning angle of an electric steering wheel; or obtaining a second target turning angle according to the output values of the left and right detecting sensors if there is no situation of missing plant, then adjusting the steering wheel in terms of controlling direction, and finally, realizing automatic row-guiding of the combine harvester.

A method of controlling a mobile agricultural machine that includes detecting a target value setting input identifying a target metric value for a quality metric representing a performance characteristic of the mobile agricultural machine and having an inverse relationship to machine speed, receiving machine data indicative of operating parameters on the mobile agricultural machine, generating, based on the machine data, a current metric value for the quality metric, determining a target machine speed based on the current metric value relative to the target metric value, and outputting a control instruction that controls the mobile agricultural machine based on the target machine speed.