In Vietnam, rice stands as a vital food source for the whole population, and because of that timely identification and management of rice plant diseases is extremely crucial. Among the diseases that appear in Vietnam, blast leaf, leaf folder, and brown spot are among the most prevalent ailments that harm rice plants, directly impacting cultivation. To address this problem, deep learning, a cutting-edge solution can be used to detect plant diseases. In this paper, we present an approach utilizing Deep learning to identify illnesses affecting rice leaves. YOLOv8 is employed and trained with various strategies of Stochastic Gradient Descent with Warm Restart (SGDR) to identify diseases on rice leaves. The result of the proposed method is assessed using 309 images. The experimental findings reveal an accuracy of 88.6%, indicating superior performance compared to alternative methods.