The significance of the agriculture domain cannot be overstated, given that it serves as the fundamental pillar of our civilization, ensuring food security as well as providing essential nutrients crucial for human growth and sustenance. Moreover, the importance of a robust food supply becomes even more critical with the continuous and substantial rise in the world’s population. Therefore, the goal of this study is to detect plant diseases in the early stages with the help of images of plants. A dataset comprising 18.5 k images was used to train the plant detection model using the YOLOv8 algorithm. The results obtained by this model reveal that the plant disease detection using YOLOv8 model is quite effective with overall precision as well as recall of nearly 100% and high mean Average Precision (mAP) score.