Early identification of crop diseases is very important. It helps farmers keep their crops healthy and protects the overall farming system. When diseases are found early, farmers can take quick action, reduce damage, and make sure there is good and steady food production. But checking crop diseases by hand is slow and not always correct. This is why we need automated methods that can work fast and give accurate results, even in different field conditions. In this study, we test three modern object detection models – YOLOv8, YOLOv11, and YOLOv12 – using two public datasets. We also check how fine-tuning YOLOv12 with the AdamW optimizer affects its performance. On Dataset D1 (7,982 images with 20 disease types), the fine-tuned YOLOv12 model reached 91.08% accuracy and 92.51% mAP@0.5. This is better than all the other models we tested. On Dataset D2 (3,683 images across 29 classes), the model’s performance dropped. This shows that the model was more likely to overfit when there were many classes but fewer images for each class. These results show that we need to choose the right training methods based on the dataset. They also highlight the need for proper regularization when training deep learning models for agricultural tasks. Overall, the results show that YOLO models work well for real-time crop disease detection. They also show that the right fine-tuning can improve how accurately the model detects diseases. The findings also give useful guidance on how to choose and modify YOLO models based on the size and difficulty of crop-disease datasets.