Classification of Plant Leaf Diseases Identification Using Deep Learning

This research addresses the imperative task of plant disease identification, specifically focusing on leaves. The approach involves a meticulous methodology encompassing image acquisition, pre-processing, and Convolutional Neural Networks (CNNs), emphasizing the crucial need for early disease detection in effective agricultural management. Recognizing the limitations of existing models, often confined to specific diseases or plants, the study advocates for a universal approach to enhance the adaptability of CNNs across diverse plant species, promoting accurate disease identification. Augmentation techniques are integrated to enhance dataset variability, ensuring the training of robust models capable of accurately identifying a broad spectrum of plant diseases. Implemented in MATLAB 2019b with its deep learning toolbox, the methodology achieves remarkable training, testing, and validation accuracies of 96.7%, 98.9%, and 98.7%, respectively, showcasing superior model performance. This research not only contributes to technological advancements in agriculture but also underscores the significance of accurate and timely disease detection for sustainable agricultural practices.