Design and Development of Leaf Disease Detection Using the ML and Open CV for Tomato Plants

This paper proposes a system for identification of the accurate disease of the plants through the captured images and provides the remedies to increase the crop yielding. Initially the images disease leaf images are taken from the internet and created a database of different plant species and the named according to their disease for the checking the accuracy and this data available in the data base is the training data. The classifier is trained with the data base to achieve the optimum accuracy of the predicted output. Convolution Neural Network (CNN) is used for the prediction of the disease that consists of different layers. To capture the leaf images in the agriculture fields with large area drones are used with high resolution cameras that gives the input to the system. 78% accuracy is obtained from the proposed system to detect the plant is health. The proposed systems use ML and Open CV to detect the plant disease.