Deep Learning for Accurate Plant Disease Classification Using ResNet50: A Comprehensive Approach

The timely and accurate identification of plant diseases is paramount for minimizing crop losses and guaranteeing food security globally. This study investigates the efficacy of deep learning in classifying plant diseases through the analysis of leaf images. A convolutional neural network (CNN) architecture is proposed, utilizing pre-trained weights from ResNet50 and subsequently fine-tuned on a dataset encompassing 38 distinct plant diseases. Data augmentation techniques are implemented to address class imbalance and bolster model robustness. The constructed model achieves a noteworthy validation accuracy of 96.49%, demonstrating its proficiency in differentiating various plant diseases. This research underscores the potential of deep learning in plant disease detection, paving the way for its real-world application within precision agriculture.