The invention relates to a method for maintaining agricultural land (110), comprising: preparing, by means of a computing device (160) comprising a processor (161) and a computer memory (162), a map of the agricultural land from previously acquired images of the agricultural land, a plurality of growing areas (120), each comprising a plurality of parallel growing rows (130), being defined in the map prepared; assigning a list of growing areas to be treated to a stand-alone vehicle (140) depending on an initial position and a final position of the stand-alone vehicle and the position of the growing areas to be treated; determining a travel plan for the stand-alone vehicle from the list of growing areas assigned to the vehicle with due regard to the orientation of the rows in the growing areas to be treated; and moving the stand-alone vehicle according to the previously established travel plan.

A method for remediation, control, and mitigation of aquatic species by killing, damaging, weakening, and or reducing the growth of living organisms or species on and under the surface of a body of water. A solar collector array powers a UVC light array comprising a plurality of UVC lights that have sufficient UVC power to control and mitigate aquatic species by killing, damaging, weakening and or reducing the growth of living organisms or species under or on the surface of a body water. The method includes remotely maneuvering the UVC light array on or under the surface of a body of water.

A method for remediation, control, and mitigation of aquatic species by killing, damaging, weakening, and or reducing the growth of living organisms or species on and under the surface of a body of water. A solar collector array powers a UVC light array comprising a plurality of UVC lights that have sufficient UVC power to control and mitigate aquatic species by killing, damaging, weakening and or reducing the growth of living organisms or species under or on the surface of a body water. The method includes remotely maneuvering the UVC light array on or under the surface of a body of water.

A computer-implemented method includes performing, using a processor onboard a vehicle, a machine learning (ML) processing on sensor input from sensors onboard an agricultural vehicle, identifying, according to a rule, a subset of data resulting from the ML processing and generating and displaying, in real-time, the subset of data to a user interface, thereby enabling a user interaction with the subset of data.

A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor includes receiving sensor input from the sensor; processing the sensor input by multiple machine learning (ML) algorithms, each using a corresponding ML model for generating labels for objects identified in the sensor input; combining labels generated by each ML algorithm to generate a super-imposed labeled sensor input frame; comparing outputs of the ML algorithms to determine similarities or differences; and using results of the comparing for improving an operational characteristic of the sensor input processing.

A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor, includes capturing, using the sensor, sensor images of a vicinity of a target object of a time interval during which a treatment is applied to the target object; processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of a treatment action in the vicinity of the target object; and providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.

A method of processing agricultural images includes comparing object detections performed by multiple image processing schemes to determine a set of ground truth images from which at least one machine learning (ML) models used by at least one ML algorithm included in the multiple image processing schemes is trained, wherein the multiple image processing schemes include two or more of (a) an image processing scheme that includes a cascade of multiple ML algorithms; (b) an image processing scheme that includes image annotation based on user feedback; or (c) an image processing scheme that includes a cascade of an ML algorithm or a computer vision (CV) algorithm and a user feedback.

A method for generating a localized data map, the method including (a) traversing an area with a machine, the machine including at least one sensor, wherein the sensor is configured to receive data; (b) collecting data of the area utilizing the sensor; and (c) communicating the data to generate a localized data map. A system and method for generating a localized turf grass data map, the method including (a) traversing an area of turf grass with an outdoor power equipment machine, the outdoor power equipment machine including at least one sensor, wherein the sensor is configured to receive data; (b) collecting turf grass data utilizing the sensor; and (c) communicating the turf grass data to generate a localized turf grass data map.

A method for generating a localized data map, the method including (a) traversing an area with a machine, the machine including at least one sensor, wherein the sensor is configured to receive data; (b) collecting data of the area utilizing the sensor; and (c) communicating the data to generate a localized data map. A system and method for generating a localized turf grass data map, the method including (a) traversing an area of turf grass with an outdoor power equipment machine, the outdoor power equipment machine including at least one sensor, wherein the sensor is configured to receive data; (b) collecting turf grass data utilizing the sensor; and (c) communicating the turf grass data to generate a localized turf grass data map.

Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.