Agriculture gains tremendous attention in India based on the sudden increase in population and food shortage. The Grape is the immensely cultivated fruit crops of India since it can grow under the tropical condition. The Grapes are proved as the lucrative and the affordable crops of India. The early identification of the disease assists the farmers to take appropriate action to prevents the crop from the disease. In addition, the severity of the disease assists in taking decisions on the proper usage of pesticides. The detection of early with high accuracy is the key step needed for the increase in agricultural production. Conventionally the grape plant disease is detected by the naked eye scrutiny of the farming experts. But the conventional approach is not practical because of the absence of experts, costly and time-consuming. The image processing method achieves serious attention among professionals in the field of disease detection. The harmful infestation is detected very easily with the image of the plant. To identify the disease correctly, the image must undergo several stages before classification. Object detection models such as YOLOv5s and YOLOv8-ACCW have been used for localizing disease areas on leaves, which support multi-class and real-time detection for field use. Hybrids based on CNNs coupled with sophisticated image processing or attention-type modules have also enhanced detection accuracy and tolerance. Even the original approaches with classical computer vision and neural networks provided an initial foundation for automated disease detection. These methods outline the revolutionary contribution of deep learning in the control of grape leaf disease, providing scalable, cost-effective, and real-time solutions that enable precision agriculture, minimize pesticide use, and enhance yield. This study emphasizes the segmentation and classification procedure for the precise prediction of plant disease.