A distributed control system for use in an assembly line grow pod includes a master controller and a hardware controller device. The master controller includes a first processor and a first memory for storing a first set of instructions that dictates plant growing operations and a second set of instructions that dictates a plurality of distributed control functions. The hardware controller device is coupled to the master controller via a plug-in network interface. The hardware controller device includes a second processor and a second memory for storing a third set of instructions that dictate a selected control function of the plurality of distributed control functions. Upon the plug-in connection, the master controller identifies an address of the hardware controller device and sends a set of parameters defining a plurality of tasks relating to the selected control function.

A smart electronic device management system is a device that is utilized to manage and control electronic devices. The device includes a housing structure that may be mounted to a surface such as a wall. A video capture device provides a live video feed of the surrounding areas while at least one environmental sensor allows monitoring of conditions in the surrounding areas. A wireless communication module allows the device to be associated with an external computing device. Various electronic devices may be connected to the device through a plurality of electrical outlets on the housing structure. A control unit allows the device to monitor and manage electronic devices that are wirelessly connected to the device or connected through the plurality of electrical outlets. The control unit is configured to calculate a sprinkler schedule using data retrieved through the wireless communication module.

This disclosure includes a method for pruning a fruit plant. An exemplary method step includes obtaining an image of the fruit plant that has branches. Next, creating exclusion zones surrounding the branches. Then pruning the fruit plant based upon the exclusion zones.

Sensor information is received from a set of sensors. First and second sets of machine monitoring data are generated from the sensor information. The first set of machine monitoring data is sent to a control system with a display in an operator compartment of a mobile machine. The second set of machine monitoring data is sent to a processing system that is separate from the control system.

In an agricultural machine, sensor signal variability is identified, over a period of time. A control system deadband is identified, based upon the sensor signal variability. A control system uses the control system deadband to control the agricultural machine.

The present application relates to the technical field of manipulators and provides an autonomous operation decision-making method of a picking manipulator. The autonomous operation decision-making method of a picking manipulator includes: acquiring sample images of fruits and branches and constructing a plurality of virtual scenes, where each virtual scene includes a picking manipulator model, a fruit model, and a branch model; in the virtual scene, determining azimuths of a target picking point and a target picking plane of an end effector model as parameters and inputting the parameters to a reward function; performing reinforcement learning training on the picking manipulator in the plurality of virtual scenes according to the reward function to determine an optimal picking action function; and controlling the picking manipulator to execute a picking task in an actual environment according to the optimal picking action function.

The present application relates to the technical field of manipulators and provides an autonomous operation decision-making method of a picking manipulator. The autonomous operation decision-making method of a picking manipulator includes: acquiring sample images of fruits and branches and constructing a plurality of virtual scenes, where each virtual scene includes a picking manipulator model, a fruit model, and a branch model; in the virtual scene, determining azimuths of a target picking point and a target picking plane of an end effector model as parameters and inputting the parameters to a reward function; performing reinforcement learning training on the picking manipulator in the plurality of virtual scenes according to the reward function to determine an optimal picking action function; and controlling the picking manipulator to execute a picking task in an actual environment according to the optimal picking action function.