Design of Domestic Plants Leaves Disease Detection Using Deep Learning - Isaac, R. Augustian; Sundaravadivel, P.; Yuvaraj, D.; Hemavathy, P.; Ramal, P. Janaki; Sankar, P. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The proposed system presents a deep learning-based solution for detecting and classifying leaf diseases in domestic plants. The proposed system employs a convolutional neural network (CNN) to automatically extract features from leaf images and classify them into different disease categories. The dataset used in the study consists of images of healthy leaves and leaves affected by bacterial spots, early blight, and late blight. Using the dataset, the CNN algorithm is trained to identify the characteristics and trends that distinguish healthy leaves from diseased leaves. Next, new photos of leaves are classified using the template as either healthy or deteriorating, and if a leaf is deteriorating, the exact disease is identified. Experiments carried out to assess the suggested approach demonstrate that it provides a high degree of accuracy in terms of both leaf disease detection and classification. The application is designed to be user-friendly and easy to use, as it is implemented as a mobile application that can be installed on a smart phone or tablet. The user can take a picture of a leaf using the camera of the device, and the application automatically processes the image and displays the name of the disease, as well as methods to cure the disease. This feature can help gardeners and farmers identify the disease and take the necessary action to prevent the illness from spreading.