Arduino based Bot for Detection of Leaf Disease

Abstract

The goal of this project is to create a robot capable of identifying plant leaf diseases through machine learning. The robot will be designed to move around plants and capture images of their leaves using a camera. The images will then be processed using machine learning algorithms to identify the presence of plant leaf diseases. Additionally, the robot will be built to avoid obstacles and prevent damage to the plants.The project will involve several stages, including dataset collection, pre-processing and training, robot design and implementation, and testing and refinement. The dataset will be collected by capturing images of leaves with and without diseases using the robot's camera. The images will be resized and their pixel values normalized before being used to train a machine learning model. The model will be trained using Keras or TensorFlow libraries.The robot will be designed using a combination of off-the- shelf and custom components. The robot will include a chassis, motors, and wheels for movement and a camera module to capture images of the leaves. The machine learning model will be integrated into the robot's software to enable it to detect plant leaf diseases. The Robot Operating System (ROS) will be used for robot control and integration.The robot's performance will be evaluated by testing it on plants with known diseases and assessing its detection accuracy. The robot's design and software will be optimized based on the test results to improve its performance.The result of this project will be a robot capable of accurately detecting plant leaf diseases, which can lead to improved plant health and increased crop yields. This project demonstrates the integration of machine learning, and robotics in agriculture to enhance plant health and crop production.In conclusion, this project will pave the way for future research and development in the field of agriculture using computer vision, machine learning, and robotics to optimize plant health and crop yield.