LEAFDISEASENET: A NOVEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR DETECTING LEAF DISEASES IN AGRICULTURAL CROPS

For productive agriculture and healthy crops, plant disease detection is essential. Conventional disease detection techniques are human, arbitrary, and error-prone and require automated solutions. Specifically, convolutional neural networks (CNNs) are deep learning models that have demonstrated great promise for identifying plant diseases; however, they are primarily model-specific for a particular kind of plant and disease, restricting them from generalizability over diverse crops and conditions. Moreover, class imbalance, environmental noise, and variability of disease symptoms make it even harder for these models to perform. In response to these problems, we present a sophisticated, integrated deep-learning framework called LeafDiseaseNet. Our proposed architecture includes a squeeze-and-excitation (SE) block with residual connections and data augmentation, enabling the model to be generalized for different plant diseases and environmental conditions. LeafDiseaseNet is evaluated on the Plant Village dataset and yields 97.68%, which outperforms current best practices regarding precision and resilience. It also exhibits good quality regarding high imbalanced class recognition and absolute environmental noise robustness, which may become promising approaches for implementing plant disease recognition in real-world agriculture situations. The framework aims to provide an efficient and scalable strategy for automating plant disease detection that can play a massive part in precision farming by making information about the disease available promptly and with sufficient time for action.