Abstract: A wide range of bacterial, viral, or fungal diseases can damage rice leaves, significantly reducing rice yield. Finding the causes of rice ailments in the leaves is essential to feeding the world's massive population. However, the recognition of rice leaf rot depends on the settings and background of the image capture. Convolutional neural network (CNN) driven model is one of the most popular study topics in rice leaf disease detection. However, the existing CNN-based models only learn large network parameters and perform much worse in terms of recognition rates on different datasets. In order to detect rice leaf illnesses, we lower the network parameters and introduce a unique CNN-based method in this research. Using a special dataset of 4199 images ofrice leaf diseases, multiple CNN-based models are trained to identify five common rice leaf diseases. The recommended model achieves the best levels of 99.78% training accuracy and 97.35% validation accuracy. An independent set of pictures of rice leaf disease is used to evaluate the effectiveness of the proposed model, yielding the highest accuracy of 97.82% and an area under the curve (AUC) of 0.99. Additionally, based on binary classification trials, our proposed model achieves identification rates of 97%, 96%, 96%, 93%, and 95% for Blast, Brown spot, Bacterial Leaf Blight, Sheath Blight, and Turgor, respectively. These results demonstrate the effectiveness and superiority of our approach over the most sophisticated CNN-based algorithms for recognizing rice leaf