Classification of Rice Leaf Diseases Based on Lightweight Deep Learning Model

Traditional methods for classifying plant diseases usually depend on manual observation, which is time-consuming, labor-intensive, and prone to human error. The rise of deep learning has greatly advanced this field by enabling more accurate and efficient classification techniques. In this paper, we introduce a novel lightweight deep learning framework that builds on the RegNetY convolutional neural network architecture by incorporating a modified Efficient Channel Attention module. This enhancement is specifically designed to improve the classification of various rice leaf diseases. Our experiments on a publicly available dataset show that the proposed approach not only boosts classification accuracy but also significantly reduces computational complexity and memory usage, making it ideal for deployment on resource-limited edge devices.