Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: A method of selective spraying in a selective-spray system that includes a spray boom attached to an agricultural vehicle, the spray boom including a plurality of cameras and a plurality of selective-spray nozzles. The method includes capturing, with each camera, a respective image of a respective region of an agricultural field, each region at a predetermined distance from the spray boom, each image associated with a respective selective-spray nozzle; automatically analyzing, with a computer, each image for a presence of at least one weed; automatically detecting, with the computer, the at least one weed in one or more images; and automatically selectively spraying one or more of the respective regions of the agricultural field using one or more of the selective-spray nozzles according to the one or more images where the at least one weed is detected.

Abstract: A system includes an agricultural vehicle, one or more cameras in mechanical communication with the agricultural vehicle, and a computer in electrical communication with the cameras. The computer is programmed to automatically analyze each image for a presence of at least one target plant using a trained machine-learning model, the trained machine-learning model having been trained with first images that include the at least one target plant and second images that do not include the at least one target plant; automatically detect, using the trained machine-learning model, the at least one target plant in a subset of the images; apply an image-selection parameter to the subset of the images to select one or more images for storage; and store the one or more images for machine-learning training in a computer storage device operably coupled to the one or more microprocessors.

Abstract: An automated computer-implemented method for real-time correction of digital images. The method includes capturing an image of an agricultural field with a camera mounted on a spray boom of an agricultural spray system, the camera operably coupled to a computer in the agricultural spray system; estimating, with the computer, a spectral power distribution of the sun based on a date and a time that the image was captured; and correcting, with the computer, a white balance of the image based on the spectral power distribution of the sun and a camera response function of the camera, the camera response function stored in computer memory operably coupled to the computer.

Abstract: There is provided a system for dynamic adaptation of a treatment applied to an agricultural field growing crops, comprising: a processor executing a code for: receiving a first and a second image from a first and a second imaging sensor, the first and second imaging sensors are located on an agricultural machine having a treatment application element(s) that applies the treatment to the agricultural field, the first and second image depict a portion of the agricultural field and overlap at an overlap region, analyzing the overlap region to compute a dynamic orientation parameter(s) of the agricultural machine, and generating instructions, according to the dynamic orientation parameter(s), for execution by a hardware component(s) associated with the agricultural machine for dynamic adaptation of the treatment applied by the treatment application element(s) to the portion of the agricultural field depicted in the first and second images to obtain a target treatment profile.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.