Abstract: Various embodiments detect and manage target vegetation in vegetation areas, including crop beds, between crop beds, and turfgrasses. In one embodiment, a machine learning model is trained to detect target vegetation in captured images. An information processing system is programmed utilizing the machine learning model. One or more images of a particular area are captured, and target vegetation is detected within the one or more images. A position of the detected target vegetation is determined within the one or more images. An applicator disposed on an agrochemical applicator system that is mapped to the position of the detected target vegetation within the one or more images is determined. The applicator is activated based on a current speed of a vehicle coupled to the agrochemical applicator system, and further based on configuration data associated with the applicator. Activating the applicator selectively applies an agrochemical to the detected target vegetation.

Abstract: Various examples are discussed for creating punched holes into a plastic mulch covering a top soil bed and precisely spraying herbicide for a limited time into each of the punched holes and on the top soil located directly below each of the punched holes in a plasticulture vegetable production operation.

Abstract: Various embodiments detect and manage target vegetation in vegetation areas, including crop beds, between crop beds, and turfgrasses. In one embodiment, a machine learning model is trained to detect target vegetation in captured images. An information processing system is programmed utilizing the machine learning model. One or more images of a particular area are captured, and target vegetation is detected within the one or more images. A position of the detected target vegetation is determined within the one or more images. An applicator disposed on an agrochemical applicator system that is mapped to the position of the detected target vegetation within the one or more images is determined. The applicator is activated based on a current speed of a vehicle coupled to the agrochemical applicator system, and further based on configuration data associated with the applicator. Activating the applicator selectively applies an agrochemical to the detected target vegetation.

Abstract: Disclosed are various examples for creating punched holes into a plastic mulch covering a top soil bed and precisely spraying herbicide for a limited time into each of the punched holes and on the top soil located directly below each of the punched holes in a plasticulture vegetable production operation.

Abstract: Systems, methods and computer-readable media are provided for controlling a variable-rate sprayer for precision agriculture. Highly efficient digital image processing enables rapid and reliable control of the variable rate sprayer. In one embodiment, image processing uses only a subset of luminance, hue, saturation and intensity textural features to provide rapid image recognition. In another embodiment, an image is decomposed into RGB components and a G is ratio determined. For example, the textural method is useful in growing season where color differentiation is difficult. The G ratio method is useful in early spring and late fall where color differentiation is possible. These rapid computationally light methods enable a mobile sprayer system to identify crop or field conditions in real-time and to dispense an appropriate amount of agrochemical in a specific section of the sprayer boom where the target has been detected as the mobile sprayer advances.

Abstract: Systems, methods and computer-readable media are provided for controlling a variable-rate sprayer for precision agriculture. Highly efficient digital image processing enables rapid and reliable control of the variable rate sprayer. In one embodiment, image processing uses only a subset of luminance, hue, saturation and intensity textural features to provide rapid image recognition. In another embodiment, an image is decomposed into RGB components and a G is ratio determined. For example, the textural method is useful in growing season where colour differentiation is difficult. The G ratio method is useful in early spring and late fall where colour differentiation is possible. These rapid computationally light methods enable a mobile sprayer system to identify crop or field conditions in real-time and to dispense an appropriate amount of agrochemical in a specific section of the sprayer boom where the target has been detected as the mobile sprayer advances.