Abstract: A dual-cap nozzle comprises a housing having a cavity to receive a liquid; a first nozzle cap attached to the housing, the first nozzle cap defining a first nozzle channel that is fluidly coupled to the cavity; and a second nozzle cap attached to the housing, the second nozzle cap defining a second nozzle channel that is fluidly coupled to the cavity. The first and second nozzle caps extend along first and second axes, respectively. An offset angle between the first and second axes is greater than or equal to about 0 degrees and less than or equal to about 45 degrees.

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

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: A configurable spray boom includes a boom frame that includes an upper support and a lower support that are separated from each other with respect to a vertical axis and extend parallel to a first axis. Camera frames are attached to the upper and lower supports, each camera frame including first and second supports that are separated from each other with respect to a second axis. Each camera frame has a vertical plane of symmetry. The camera frames are evenly spaced, with respect to the first axis, along the boom frame. In a first configuration a respective camera is mounted on the first support such that the respective camera is oriented in a first direction. In a second configuration the respective camera is mounted on the second support such that the respective camera is oriented in a second direction.

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: A retrofit dual sprayer system comprises an agricultural vehicle having a broadcast tank mounted thereon, the broadcast tank having a first volumetric capacity for holding general-application liquid chemical(s) for preventing undesirable plants from growing in an agricultural field, a selective-spot spray (SSP) tank retrofitly mounted on the agricultural vehicle, the SSP tank having a second volumetric capacity for holding specific liquid chemical(s) for treating one or more target weeds growing in the agricultural field, the second volumetric capacity greater than or equal to about 5% of the first volumetric capacity and less than or equal to about 30% of the first volumetric capacity, a spray boom attached to the agricultural vehicle, the spray boom including broadcast nozzles mounted on the spray boom, the broadcast nozzles fluidly coupled to the broadcast tank, and SSP nozzles retrofitly mounted on the spray boom, the SSP nozzles fluidly coupled to the SSP tank.

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.