Enhancing rice plant disease detection through transfer learning and image segmentation with YOLOv11

Rice is a staple food globally, yet its productivity is often threatened by diseases such as blast and brown spot. Traditional diagnostic methods relying on human observation are prone to delays and inaccuracies. This study introduces an automated detection system that utilizes YOLOv11-seg to improve the accuracy and efficiency of rice disease identification. The model integrates object detection and instance segmentation, is trained on over 6,000 annotated images covering six categories (five disease types and healthy leaves), and leverages transfer learning from COCO weights. Experimental results show that the model achieved a bounding box mAP@50 of 0.607 and a segmentation mAP@50 of 0.564, with F1-scores of 0.62 and 0.59, respectively. The highest detection accuracy was recorded for healthy leaves (86%), while segmentation performance declined on visually similar classes such as Brown Spot and Sheath Blight. Overfitting was observed during training, with a 15–20% gap between training and validation metrics. These findings demonstrate the model's potential for real-time field application in precision agriculture. Future improvements should focus on enhancing spatial accuracy and robustness through synthetic data generation and architectural optimization.