Explainable Deep Transfer Learning Framework for Rice Leaf Disease Diagnosis and Classification

Rice plays a vital role in the food stock. But sometimes this crop leaf falls into disease. And, the amount of food consumed will decrease due to leaf disease. So, discovering the rice leaf disease is necessary to improve rice productivity. Currently, many researchers use deep learning methods to solve this problem. Unfortunately, their research results were less accurate. In this paper, we construct transfer learning models to diagnose and categorize illnesses affecting rice leaves. To further improve the model performance, we construct three ensemble learning models to combine various architectures. In order to bring transparency to the disease diagnostic process, we explore the explainable AI (XAI) problem of the visual object detector and integrate Gradient-weighted Class Activation Mapping (Grad- CAM) into three ensemble models to generate explanations for individual object detections for assessing performance. The results of Ensemble Learning indicate that merging different architectures can be effective in disease diagnosis, as evidenced by their best accuracy of 99.78% which is better than other stateof- the-art works. This research demonstrates that the integration of deep learning and transfer learning models yields improved prediction interpretability and classification accuracy of rice leaf disease. So, we established a dependable method of deep, transfer, and ensemble learning for the diagnosis of diseases affecting rice leaves.