Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network
When plants and crops are affected by pests it affects the agricultural p roduction of the country. Us ually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expens ive and inaccurate. Automatic detection using image processing techniques provide fast and accurate results. This paper is concerned with a new approach to the development of p lant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Advances in computer v ision present an opportunity to expand and enhance th e practice of precise p lant protection and extend the market of computer vision applications in the field of precision agricu lture. Novel way of training and the methodology used facilitate a quick and easy system implementation in pract ice. A ll essential steps required for implement ing this disease recognition model are fu lly described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts a deep learning framework to perform the deep CNN train ing. This method paper is a new approach in detecting plant diseases using the deep convolutional neural network trained and fine tuned to fit accurately to the database of a plant's leaves that was gathered independently for d iverse plant diseases. The ad vance and novelty of the developed model lie in its simplicity; healthy leaves and background images are in line with other classes, enabling the mode l to distinguish between diseased leaves and healthy ones or from the environment by using deep CNN.