Automated processes in the field of agriculture have become increasingly reliable and efficient. Farmers face many difficulties due shortage of manpower since it is a time-consuming and tedious task. Robotic systems integrated with various control methods can be very useful in doing repetitive work, such as the seed-sowing process where the same movement is continuous. This project utilizes a robot that acts as a cost-effective system to detect weeds in agricultural fields. An automated system is to develop a trainable-automatic robot that helps in removing unwanted weeds on agricultural fields using gestures to control a three-axis robotic arm to do the necessary work using Raspberry Pi. The arm is placed on a rover and an optical sensor of low resolution is placed on it to detect the difference between plants and weeds using Machine Learning. The arm is designed to perform repetitive motions to do the necessary work. If a plant is detected, the robot will treat as a plant and not harm it, the system provides an indication of a plant, or else it would display that a weed has been detected. The arms of the rover are activated and hence the weeds will be removed from the field. The robotic arm present on the rover would be tested and evaluated under normal environmental conditions. The Raspberry Pi transmits signals to control location as well as detect the presence or absence of weed, it is also used to ensure proper movement of a robot. This type of robot is used in the crop field to cut the weeds as per the user’s command.

Keywords : Weed Identification, Raspberry Pi 4, Machine Learning, Robotic Arm, Accuracy, Convolutional Neural Networks, Robotic Movement, Training Model, Dataset.