A residue detection and implement control system and method are disclosed for an agricultural implement. The system includes a source of environment data and image data of an imaged area of a crop field containing residue. The system includes a data store containing a plurality of image processing methods and at least one controller that processes the image data according to one or more image processing instruction sets. The controller selects one or more of the image processing methods based on the environment data, and processes the image data using the selected image processing instruction(s) to determine a value corresponding to residue coverage in the imaged area of the field. The controller adjusts the configuration of the agricultural implement to respond to the amount and type of residue detected.

A method of operating a tillage implement includes performing a first calibration of the tillage implement based on a curated library, propelling the tillage implement through a field, capturing an image of the field with a camera carried by the tillage implement, comparing information from the captured image with the curated library, identifying residue based on the comparison of the information from the captured image with the curated library, modifying the curated library based on the captured image, and performing a second calibration of the tillage implement based on the modified library. The curated library includes a correlation of observed data with material properties of soil and/or residue, and may initially be based on information from other fields or prior times. The tillage implement has at least one ground-engaging tilling assembly, and propelling the tillage implement through the field causes the tilling assembly to work the soil.

The invention relates to an end-effector device and automated selective thinning system. The system includes vision acquisition hardware, kinematic targeting and heuristic programming, a robotic arm, and a pomologically designed end-effector. The system is utilized to improve efficiency for the fruit-thinning process in a tree orchard, such as peach thinning. By automating the mechanical process of fruit thinning, selective fruit-thinners can eliminate manual labor inputs and further enhance favorable blossom removal. Automation used in conjunction with a heuristic approach provides improvements to the system. The system may also be configured as a robotic arm or as a handheld system by including a battery and switching microcontroller with handle or wrist straps. Handheld thinning devices that are mechanical in nature may also be part of the system.

The invention relates to an end-effector device and automated selective thinning system. The system includes vision acquisition hardware, kinematic targeting and heuristic programming, a robotic arm, and a pomologically designed end-effector. The system is utilized to improve efficiency for the fruit-thinning process in a tree orchard, such as peach thinning. By automating the mechanical process of fruit thinning, selective fruit-thinners can eliminate manual labor inputs and further enhance favorable blossom removal. Automation used in conjunction with a heuristic approach provides improvements to the system. The system may also be configured as a robotic arm or as a handheld system by including a battery and switching microcontroller with handle or wrist straps. Handheld thinning devices that are mechanical in nature may also be part of the system.

A method of real-time plant selection and removal from a plant field including capturing a first image of a first section of the plant field, segmenting the first image into regions indicative of individual plants within the first section, selecting the optimal plants for retention from the first image based on the first image and the previously thinned plant field sections, sending instructions to the plant removal mechanism for removal of the plants corresponding to the unselected regions of the first image from the second section before the machine passes the unselected regions, and repeating the aforementioned steps for a second section of the plant field adjacent the first section in the direction of machine travel.

A method of real-time plant selection and removal from a plant field including capturing a first image of a first section of the plant field, segmenting the first image into regions indicative of individual plants within the first section, selecting the optimal plants for retention from the first image based on the first image and the previously thinned plant field sections, sending instructions to the plant removal mechanism for removal of the plants corresponding to the unselected regions of the first image from the second section before the machine passes the unselected regions, and repeating the aforementioned steps for a second section of the plant field adjacent the first section in the direction of machine travel.

A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

A residue detection and implement control system and method are disclosed for an agricultural implement. The system includes a source of environment data and image data of an imaged area of a crop field containing residue. The system includes a data store containing a plurality of image processing methods and at least one controller that processes the image data according to one or more image processing instruction sets. The controller selects one or more of the image processing methods based on the environment data, and processes the image data using the selected image processing instruction(s) to determine a value corresponding to residue coverage in the imaged area of the field. The controller adjusts the configuration of the agricultural implement to respond to the amount and type of residue detected.

A residue detection and implement control system and method are disclosed for an agricultural implement. The system includes a source of environment data and image data of an imaged area of a crop field containing residue. The system includes a data store containing a plurality of image processing methods and at least one controller that processes the image data according to one or more image processing instruction sets. The controller selects one or more of the image processing methods based on the environment data, and processes the image data using the selected image processing instruction(s) to determine a value corresponding to residue coverage in the imaged area of the field. The controller adjusts the configuration of the agricultural implement to respond to the amount and type of residue detected.