A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

Systems and methods here may include a vehicle with automated subcomponents for harvesting delicate items such as berries. In some examples, the vehicle includes a targeting subcomponent and a harvesting subcomponent. In some examples, the targeting subcomponent utilizes multiple cameras to create three-dimensional maps of foliage and targets. In some examples, identifying targets may be done remotely from the harvesting machine, and target coordinates communicated to the harvesting machine for robotic harvesting.

The automated trimming of an untrimmed plant stem comprises a device for transporting an untrimmed plant stem to an imaging stage, using a camera to capture at least one image of the plant stem, sending the at least one image to a central processor which creates a trim map which constructs a pattern for moving a cutting armature along the plant stem, and trimming the plant stem by cutting structure of the cutting armature to change the untrimmed plant stem to a trimmed plant stem.

A communication system facilitates a closed loop, two-way communication network between machines at a worksite and a remote processing facility. A management and control system receives operations data and generates recommended adjustments to the worksite operations. A manager system provides manager outputs over the communication network to adjust operations of the machines at the worksite based on the recommended adjustments.

Systems and methods for determining harvest timing for a cart within an assembly line grow pod include identifying a type of the plant matter positioned within a cart, detecting at least one of a plant matter weight of the plant matter with a weight sensor, a plant matter height of the plant matter with a distance sensor, and a chlorophyll level of the plant matter with a camera, determining that the at least one of the detected plant matter weight, the detected plant matter height, and the detected chlorophyll level satisfies a harvest time parameters, and in response to determining that the detected plant matter weight, the detected plant matter height, and the detected chlorophyll level satisfy the harvest time parameters, directing the cart to a harvester system.

A communication system facilitates a closed loop, two-way communication network between machines at a worksite and a remote processing facility. A management and control system receives operations data and generates recommended adjustments to the worksite operations. A manager system provides manager outputs over the communication network to adjust operations of the machines at the worksite based on the recommended adjustments.

A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

Systems and methods here may include a vehicle with automated subcomponents for harvesting delicate items such as berries. In some examples, the vehicle includes a targeting subcomponent and a harvesting subcomponent. In some examples, the targeting subcomponent utilizes multiple cameras to create three-dimensional maps of foliage and targets. In some examples, identifying targets may be done remotely from the harvesting machine, and target coordinates communicated to the harvesting machine for robotic harvesting.

A robotic fruit picking system includes an autonomous robot that includes a positioning subsystem that enables autonomous positioning of the robot using a computer vision guidance system. The robot also includes at least one picking arm and at least one picking head, or other type of end effector, mounted on each picking arm to either cut a stem or branch for a specific fruit or bunch of fruits or pluck that fruit or bunch. A computer vision subsystem analyses images of the fruit to be picked or stored and a control subsystem is programmed with or learns picking strategies using machine learning techniques. A quality control (QC) subsystem monitors the quality of fruit and grades that fruit according to size and/or quality. The robot has a storage subsystem for storing fruit in containers for storage or transportation, or in punnets for retail.

A method and control system are disclosed for adjusting orientations of knockdown rollers for sugarcane harvesters. An upper knockdown roller may be moved along a first adjustment path using a first actuator. A lower knockdown roller may be moved along a second adjustment path using a second actuator. A substantially tangential alignment may be maintained for an outer diameter of the upper knockdown roller and an outer diameter of the lower knockdown roller with respect to a reference point along a cutting path of the base cutter assembly, during or after movement of the upper and lower knockdown rollers.