Abstract: A multimode autonomous farming machine capable of performing uniform and variable plant treatments including defoliation is described. This farming machine is capable of operating in various operational modes and treatment configurations, such as broadcast or spot mode, and uniform or variable treatment mode. In addition, the machine adjusts its mode and treatment configuration based on various factors such as present conditions in the operating environment, conditions of the farming machine itself, the treatment plan, and farming objectives. The autonomous machine uses a control system to input various data measurements to inform the appropriate mode(s) and treatment. Dynamic treatment mode selection increases efficiency and resource production in an agricultural field.

Abstract: A multimode autonomous farming machine capable of performing uniform and variable plant treatments including defoliation is described. This farming machine is capable of operating in various operational modes and treatment configurations, such as broadcast or spot mode, and uniform or variable treatment mode. In addition, the machine adjusts its mode and treatment configuration based on various factors such as present conditions in the operating environment, conditions of the farming machine itself, the treatment plan, and farming objectives. The autonomous machine uses a control system to input various data measurements to inform the appropriate mode(s) and treatment. Dynamic treatment mode selection increases efficiency and resource production in an agricultural field.

Abstract: The calibration system of the farming machine receives images from the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate the camera array, the system determines a relative pose between pairs of cameras by extracting relative position and orientation characteristics from visual information in images captured by the camera pairs. The calibration system can determine that a pair of cameras is in a swapped state by comparing the relative pose of the pair of cameras to an expected pose of the pair of cameras. The calibration system adjusts the pair to remedy the swapped state.

Abstract: The calibration system of the farming machine receives images from the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate the camera array, the system determines a relative pose between pairs of cameras by extracting relative position and orientation characteristics from visual information in images captured by the camera pairs. The calibration system determines a calibration error in part by propagating the relative poses between camera pairs. The calibration system may perform automated self-calibration by adjusting one or more of the cameras in the camera array, or may transmit remedial instructions to an operator to adjust the one or more cameras in the camera array.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Abstract: The calibration system of the farming machine receives images from the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate the camera array, the system determines a relative pose between pairs of cameras by extracting relative position and orientation characteristics from visual information in images captured by the camera pairs. The calibration system determines a calibration error in part by propagating the relative poses between camera pairs. The calibration system may perform automated self-calibration by adjusting one or more of the cameras in the camera array, or may transmit remedial instructions to an operator to adjust the one or more cameras in the camera array.

Abstract: The calibration system of the farming machine receives images from each camera of the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate a pair of cameras including a first camera and second camera, the calibration system determines a relative pose between the pair of cameras by extracting relative position and orientation characteristics from visual information in both an image received from the first camera and an image received from the second camera. The calibration system identifies a calibration error for the pair of cameras based on a comparison of the relative pose with an expected pose between the first pair of cameras. The calibration system transmits a notification to an operator of the farming machine that describes the calibration error and instructions for remedying the calibration error.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes a pair of image sensors for capturing images of a plant. The image sensors are different, and their output images are used to generate a depth map to improve the plant identification process. A control system identifies a plant using the depth map. The control system captures images, identifies a plant, and actuates a treatment mechanism in real time.

Abstract: The calibration system of the farming machine receives images from each camera of the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate a pair of cameras including a first camera and second camera, the calibration system determines a relative pose between the pair of cameras by extracting relative position and orientation characteristics from visual information in both an image received from the first camera and an image received from the second camera. The calibration system identifies a calibration error for the pair of cameras based on a comparison of the relative pose with an expected pose between the first pair of cameras. The calibration system transmits a notification to an operator of the farming machine that describes the calibration error and instructions for remedying the calibration error.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes a pair of image sensors for capturing images of a plant. The image sensors are different, and their output images are used to generate a depth map to improve the plant identification process. A control system identifies a plant using the depth map. The control system captures images, identifies a plant, and actuates a treatment mechanism in real time.

Abstract: A path of travel used by an autopilot operation system for auto-guidance of a mobile machine is defined, transparently to a human operator, in response to the human operator engaging and disengaging operation of an implement coupled with the mobile machine. The auto-guidance of the mobile machine is activated, transparently to the human operator, in response to the human operator engaging the implement a second time.

Abstract: A path of travel used by an autopilot operation system for auto-guidance of a mobile machine is defined, transparently to a human operator, in response to the human operator engaging and disengaging operation of an implement coupled with the mobile machine. The auto-guidance of the mobile machine is activated, transparently to the human operator, in response to the human operator engaging the implement a second time.

Abstract: Auto-guidance of a mobile machine is provided that does not require a graphical user interface display. Human interactions with the auto-guidance system of a mobile machine are performed solely with one or more electromechanical switches of the auto-guidance system while the mobile machine is operating. A first point for auto-guidance of a mobile machine is set by an auto-guidance system in response to a first electromechanical switch input. A second point for auto-guidance of the mobile machine is set by the auto-guidance system in response to a second electromechanical switch input after movement of the mobile machine from the first point. The auto-guidance system activates auto-guidance of the mobile machine along a path defined by the first point and the second point. The setting of the first point, the setting of the second point, and the activating are performed by one or more hardware processors.