A route generation method is a method for generating a target route for a work vehicle autonomously traveling in a travel area, and executes acquiring an inclination angle of the travel area, setting a travel speed of the work vehicle for the travel area on a basis of the inclination angle of the travel area, and generating the target route including speed information of the travel speed set for the travel area.

An apparatus includes a frame, a drive assembly supported by the frame, an electronic system supported by the frame, and a cleaning assembly coupled to the frame. The drive assembly is configured to move the frame along a surface. The cleaning assembly is configured to engage the surface to transfer detritus from the surface to a storage volume supported by the frame. The electronic system has at least a processor and a memory. The processor is configured to define a path along which the drive assembly travels and is configured to redefined a path along which the drive assembly travels based on at least one signal received from at least one sensor.

A method of area coverage planning with replenishment planning includes receiving information of a boundary of the work area, location information of one or more refill stations, and information of a current amount of the material left in the autonomous vehicle, laying a plurality of tracks within the boundary of the work area so as to minimize a total distance of the plurality of tracks, generating a coverage trajectory, and based on (i) the coverage trajectory, (ii) the location information of the one or more refill stations, (iii) the current amount of the material left in the autonomous vehicle, and (iv) a nominal full amount and a nominal consumption rate of the material by the autonomous vehicle, determining one or more logistic points along the coverage trajectory at which a remaining amount of the material reaches a threshold, for each logistic point, generating a replenishment trajectory.

Disclosed herein is an apparatus for facilitating managing cultivation of crops based on monitoring the crops. Further, the apparatus comprises an apparatus body, cameras, light sensors, a processing unit, and a communication interface. Further, the cameras generate a measurement of a crop and a field portion. Further, the light sensors generate an environment measurement of an environment of the apparatus. Further, the processing unit analyzes the environment measurement, determines a factor affecting the measurement, and generates a calibrating factor for the cameras. Further, the calibrating factor facilitates compensating the affecting of the factor in the measurement. Further, the cameras calibrate a camera parameter of the cameras based on the calibrating factor to generate the measurement. Further, the processing unit analyzes the measurement and generates a status of the crop. Further, the communication interface transmits the status to a device.

A map generator generates a replanting map designating a particular area in a field in which it is recommended to add additional seeds. A function of the agricultural machine is then controlled based at least in part on the replanting map so as to facilitate planting additional seeds in the designated particular area.

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.