A Deep Learning Approach for Leaf Disease Detection

To guarantee agricultural output and stop the spread of disease, early detection and treatment of plant diseases are essential. The problem of classifying plant leaf diseases is addressed in this article using a novel approach based on the EfficientNetV2 model. The model's complex structure makes it possible to accurately diagnose a variety of diseases by effectively differentiating them based on minute variations in leaf characteristics. This study shows that deep learning approaches can accurately detect disease patterns and symptoms, which can be used as a tool to detect and provide evidence of future changes in the disease state. Our recommended method outperforms conventional disease management techniques in terms of performance effectiveness and quality by leveraging cutting-edge technologies. EfficientNetV2 easily outperforms conventional deep learning techniques on all major evaluation metrics, according to a comparative analysis with a baseline CNN model. In addition to the technology, the approach has important ramifications for the agricultural methodology framework as a whole. By empowering stakeholders to automatically and precisely identify diseases that can improve crop health and yield, our method has the potential to completely transform disease management strategies. In order to ensure food security and sustainability globally, this article demonstrates how the clever integration of new technologies with agricultural systems has the potential to revolutionize active disease management.