Abstract: The system can include a detection model; an optional camera system; and an optional control system. The system can function to detect plants within a field. Additionally, the system can function to facilitate an agriculture operation(s) based on the positions of plants within the field. Variants of the system can be configured to (autonomously) perform and/or facilitate agriculture operations which can include: agent dispersal (e.g., solid agent dispersal), fluid spraying, crop imaging (e.g., crop data collection), side dressing, weeding (e.g., mechanical actuation, targeted laser ablation, etc.), harvesting, planting, tilling, fertilizing, irrigating, and/or any other suitable operation(s). Variants of the system and/or method can be used to facilitate detection and/or agriculture operations for single crops, multi-crops (e.g., crop doubles, where agriculture operations may be based on stem proximity), ground cover plants, weeds, and/or agriculture operations in any other suitable scenarios.

Abstract: One variation of a method for executing agricultural actions on an agricultural field includes: based a type of a crop planted on the agricultural field and a maturity of the crop, setting a nominal blade gap width of a weeding module installed in an autonomous agricultural vehicle; and, at the autonomous agricultural vehicle, while autonomously traversing the agricultural field, detecting a first target plant passing under the autonomous agricultural vehicle and aligned with the weeding module, triggering the weeding module to open a set of weeding blades to the nominal blade gap width at a nominal blade speed in response to the first target plant approaching the weeding module, and triggering the weeding module to close the set of weeding blades in response to the first target plant passing the weeding module.

Abstract: One variation of a method for executing agricultural actions on an agricultural field includes: based a type of a crop planted on the agricultural field and a maturity of the crop, setting a nominal blade gap width of a weeding module installed in an autonomous agricultural vehicle; and, at the autonomous agricultural vehicle, while autonomously traversing the agricultural field, detecting a first target plant passing under the autonomous agricultural vehicle and aligned with the weeding module, triggering the weeding module to open a set of weeding blades to the nominal blade gap width at a nominal blade speed in response to the first target plant approaching the weeding module, and triggering the weeding module to close the set of weeding blades in response to the first target plant passing the weeding module.

Abstract: A system for crop detection and/or analysis can include a set of sensors; a plurality of Light-Emitting Diodes (LEDs); a computing system; a housing; and a set of thermal components. Additionally or alternatively, the system can include any other suitable components. A method for crop detection and/or analysis can include any or all of: activating a set of lighting elements; collecting a set of images; and processing the set of images. Additionally or alternatively, the method can include any or all of: actuating and/or otherwise activating a set of implements; training and/or updating (e.g., retraining) a set of models used in processing the set of images; and/or any other suitable processes.

Abstract: A system for crop detection and/or analysis can include a set of sensors; a plurality of Light-Emitting Diodes (LEDs); a computing system; a housing; and a set of thermal components. Additionally or alternatively, the system can include any other suitable components. A method for crop detection and/or analysis can include any or all of: activating a set of lighting elements; collecting a set of images; and processing the set of images. Additionally or alternatively, the method can include any or all of: actuating and/or otherwise activating a set of implements; training and/or updating (e.g., retraining) a set of models used in processing the set of images; and/or any other suitable processes.

Abstract: A method for detecting real lateral locations of target plants includes: recording an image of a ground area at a camera; detecting a target plant in the image; accessing a lateral pixel location of the target plant in the image; for each tool module in a set of tool modules arranged behind the camera and in contact with a plant bed: recording an extension distance of the tool module; and recording a lateral position of the tool module relative to the camera; estimating a depth profile of the plant bed proximal the target plant based on the extension distance and the lateral position of each tool module; estimating a lateral location of the target plant based on the lateral pixel location of the target plant and the depth profile of the plant bed surface proximal the target plant; and driving a tool module to a lateral position aligned with the lateral location of the target plant.

Abstract: A system for crop detection and/or analysis can include a set of sensors; a plurality of Light-Emitting Diodes (LEDs); a computing system; a housing; and a set of thermal components. Additionally or alternatively, the system can include any other suitable components. A method for crop detection and/or analysis can include any or all of: activating a set of lighting elements; collecting a set of images; and processing the set of images. Additionally or alternatively, the method can include any or all of: actuating and/or otherwise activating a set of implements; training and/or updating (e.g., retraining) a set of models used in processing the set of images; and/or any other suitable processes.

Abstract: The system can include: an implement frame and a set of modular assemblies. The system can optionally include a vehicle and a set of effectors. However, the system can additionally or alternatively include any other suitable set of components. The system can function to facilitate automated and/or perception-based weeding and/or other automated agricultural operations. Additionally, the system can function to collect plant-level data on crops during implement operation. However, the system can additionally or alternatively be configured to provide any other suitable functionality.

Abstract: A method for detecting real lateral locations of target plants includes: recording an image of a ground area at a camera; detecting a target plant in the image; accessing a lateral pixel location of the target plant in the image; for each tool module in a set of tool modules arranged behind the camera and in contact with a plant bed: recording an extension distance of the tool module; and recording a lateral position of the tool module relative to the camera; estimating a depth profile of the plant bed proximal the target plant based on the extension distance and the lateral position of each tool module; estimating a lateral location of the target plant based on the lateral pixel location of the target plant and the depth profile of the plant bed surface proximal the target plant; and driving a tool module to a lateral position aligned with the lateral location of the target plant.

Abstract: A method for weeding crops includes, at an autonomous machine: recording an image at the front of the autonomous machine; detecting a target plant and calculating an opening location for the target plant longitudinally offset and laterally aligned with the location of the first target plant; driving a weeding module to laterally align with the opening location; tracking the opening location relative to a longitudinal reference position of the weeding module; when the weeding module longitudinally aligns with the first opening location, actuating the blades of the first weeding module to an open position; recording an image proximal to the weeding module; and in response to detecting the blades of the weeding module in the open position: calculating an offset between the opening location and a reference position of the weeding module, based on the image; and updating successive opening locations calculated by the autonomous machine based on the offset.

Abstract: Methods and systems for controlling robotic actions for agricultural tasks are disclosed which use a spacing-aware plant detection model. A disclosed method, in which all steps are computer-implemented, includes receiving, using an imager moving along a crop row, at least one image of at least a portion of the crop row. The method also includes using the at least one image, a plant detection model, and an average inter-crop spacing for the crop row to generate an output from the plant detection model. The plant detection model is spacing aware in that the output of the plant detection model is altered or overridden based on the average inter-crop spacing. The method also includes outputting a control signal for the robotic action based on the output from the biased plant detection model. The method also includes conducting the robotic action for the agricultural task in response to the control signal.

Abstract: A method includes: at an autonomous vehicle and during a first operating period, capturing a first set of images of a plant; calculating a location of the plant based on the first set of images; extracting an initial value of a plant metric of the plant based on the first set of images; predicting a predicted value of the plant metric of the plant at a time based on the initial value of the plant metric of the plant and a set of global condition data. The method also includes, at the autonomous vehicle and during a second operating period concurrent with the time: capturing a second set of images of the plant; identifying the plant based on the second set of images and the location of the plant; and executing an agricultural operation on the plant based on the predicted value of the plant metric.

Abstract: A method for detecting real lateral locations of target plants includes: recording an image of a ground area at a camera; detecting a target plant in the image; accessing a lateral pixel location of the target plant in the image; for each tool module in a set of tool modules arranged behind the camera and in contact with a plant bed: recording an extension distance of the tool module; and recording a lateral position of the tool module relative to the camera; estimating a depth profile of the plant bed proximal the target plant based on the extension distance and the lateral position of each tool module; estimating a lateral location of the target plant based on the lateral pixel location of the target plant and the depth profile of the plant bed surface proximal the target plant; and driving a tool module to a lateral position aligned with the lateral location of the target plant.

Abstract: A method for weeding crops includes, at an autonomous machine: recording an image at the front of the autonomous machine; detecting a target plant and calculating an opening location for the target plant longitudinally offset and laterally aligned with the location of the first target plant; driving a weeding module to laterally align with the opening location; tracking the opening location relative to a longitudinal reference position of the weeding module; when the weeding module longitudinally aligns with the first opening location, actuating the blades of the first weeding module to an open position; recording an image proximal to the weeding module; and in response to detecting the blades of the weeding module in the open position: calculating an offset between the opening location and a reference position of the weeding module, based on the image; and updating successive opening locations calculated by the autonomous machine based on the offset.