Abstract: A system for spot or band spraying of a treatment applied to target areas includes nozzle units that are connected to a liquid conducting member and spaced at horizontal intervals. Each nozzle unit includes first and second nozzles configured to spray droplets around opposing spraying vectors. For each target area, a spraying vector of a first nozzle of a first nozzle unit crosses a spraying vector of a second nozzle of an adjacent second nozzle unit. When a boom is at an operation height, each location within a target area receives a predefined cumulative volume of treatment, and spray patterns of the first and second nozzles of adjacent nozzle units overlap by at least 10%. When the boom is within 5% deviation from the predetermined operation height, each location within the target area receives a predefined minimum percentage of the predefined cumulative value of the treatment.

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: The present invention discloses a method for selective crop management in real time.

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: A system for monitoring plants, the system comprising: one or more sound collectors configured to receive and generate signals based on sounds that plants make; and a computer system configured to receive and process the signals to provide a profile of a plant producing the sounds.