Leaf Disease Detection and Smart Spraying Robot using Artificial Intelligence

For many countries, like India with large numbers of its people dependent on the land to sustain them in some way or another, agriculture is a key part of their economy. An important current question in agriculture is the conclusion of timely and accurate pest and disease diagnosis by humans. These diseases negatively impact both crop outcome as well as product quality We address this issue in this paper by proposing a total system for early leaf disease detection and targeted spraying based on artificial intelligence (AI). We hold the belief that this study can bring forward an effective way for disease management which cares less about chemicals, and therefore reduces the environmental burden. The methods also provide an alternative approach to agricultural sustainability hidden away in common sense. The ultimate goal is to develop a system that combines computer vision, deep learning, and robotic platforms into a completely automated diagnosis and treatment system. Learning based approaches usually utilize CNNs (convolutional neural networks) to distinguish between different plant leaf diseases using high-resolution images provided by board-mounted cameras. The system is capable of detecting early-stage infections effectively by providing the model with a trained data set of labeled sick and healthy leaf samples. Once a disease is discovered, the robot switches on smart spraying that sprays only infected areas, minimizing the use of pesticides and harm to the environment--while an app interface allows for real-time alerts on diseases and remote monitoring from anywhere. This project offers a cost-effective yet scalable solution to the agricultural industry that can be effectively used to alleviate and ultimately eliminate disease related health hazards for farmers, whilst making the most of sustainable crop handling practices.