Rice Leaf Disease Diagnosis Using Dense EfficientNet Model

Global food security and agricultural sustainability are seriously threatened by the paddy leaf diseases that is rapidly spread. Four of the most common widespread disease that affect rice leaf are Hispa, brown spot, Leaf blast and as well as Healthy leaf are identified. Quickly and correctly identifying a specific disease in order to implement effective strategies for its control and treatment. After the dataset performed the necessary pre-processing, a variety of deep learning algorithms, comprising VGG19, Resnet50, and InceptionV3, were applied on it. Our deep learning model is based on the EfficientNetB3 architecture, which has demonstrated excellent performance in image analysis applications. The EfficientNetB3 model is trained on the collected dataset, employing transfer learning to leverage pre-trained weights and accelerate convergence. To enhance the discriminative power of the model, employ feature extraction techniques. These techniques extract relevant features from the images and provide valuable information for disease detection. The integration of deep learning and feature extraction techniques, specifically using EfficientNetB3 demonstrates the potential of modern technologies in the field of agriculture. The ultimate objective of this integrated technique is to enable agricultural precision by providing farmers with a technology that is capable of identifying and monitoring plant diseases precisely and efficiently. It aids to sustainable agricultural production by providing focused interventions and minimising resource use. In summary, the originality in this work is found in the careful coupling of cutting-edge deep learning architecture, transfer learning, and feature extraction approaches, showcasing the advances being made in disease identification for the improvement of agricultural sustainability.