Abstract: Disclosed herein are methods, devices, modules, and systems which may be employed to accurately track, and subsequently target, objects of interest relative to a moving body, such as a vehicle. An object tracking method may be implemented by a detection system with a sensor coupled to the moving body. A targeting system may be used to target objects tracked by the detection system, such as for automated crop cultivation or maintenance. Devices disclosed herein may be configured to locate, identify, and autonomously target a weed with a beam, such as a laser beam, which may burn or irradiate the weed. The methods, devices, modules, and systems may be used for agricultural crop management or for at-home weed control.

Abstract: Disclosed herein are methods, devices, modules, and systems which may be employed to accurately track, and subsequently target, objects of interest relative to a moving body, such as a vehicle. An object tracking method may be implemented by a detection system with a sensor coupled to the moving body. A targeting system may be used to target objects tracked by the detection system, such as for automated crop cultivation or maintenance. Devices disclosed herein may be configured to locate, identify, and autonomously target a weed with a beam, such as a laser beam, which may burn or irradiate the weed. The methods, devices, modules, and systems may be used for agricultural crop management or for at-home weed control.

Abstract: Systems and methods for reducing latency of targeting and manipulating of an object of interest is provided. The method includes receiving an image of the object of interest, generating a predicted location of the object of interest based on the received image, applying an offset learned by a machine learning model, the offset representing a difference between a prediction system and a targeting system, causing an adjustment to an implement, and targeting the object of interest with the adjusted implement after the offset is applied.

Abstract: Systems and methods for autonomous crop maintenance and seedline tracking in agricultural environments. The methods involve determining crop density within a field, generating kernel density estimations, and identifying seedline locations by analyzing local minima in crop density and their persistence. Virtual bands are generated around the seedlines to focus maintenance efforts, such as weeding or thinning, on these areas while ignoring out-of-band regions.

Abstract: Systems and methods for autonomously thinning crops in agricultural fields. An autonomous plant targeting system identifies individual crops within the region, evaluates parameters such as spacing, health, and size, and selects specific crops for thinning based on these parameters. The system is capable of designating crop boundaries around individual crops and selecting target crops for removal or eradication, such as by laser irradiation, without affecting surrounding plants.

Abstract: Disclosed herein are methods, devices, modules, and systems which may be employed to accurately track, and subsequently target, objects of interest relative to a moving body, such as a vehicle. An object tracking method may be implemented by a detection system with a sensor coupled to the moving body. A targeting system may be used to target objects tracked by the detection system, such as for automated crop cultivation or maintenance. Devices disclosed herein may be configured to locate, identify, and autonomously target a weed with a beam, such as a laser beam, which may burn or irradiate the weed. The methods, devices, modules, and systems may be used for agricultural crop management or for at-home weed control.