Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. In this paper, a Deep Learning technique for the accurate detection and classification of rice leaf disease is proposed. The residual attention based Efficient Net-U-Net is proposed for the process of segmentation. For the detection and classification of the rice leaf disease, Convolutional Neural Network (CNN) is proposed. The performance of the proposed work is evaluated in terms of accuracy, precision, recall and F1 score. The proposed work obtains the highest accuracy of 89.35%,