An Optimized YOLO11n Model with Multi-Stage Attention Mechanisms for Pitaya Disease Detection

At present, the diagnosis of pitaya diseases is chiefly dependent on human expertise, a process that is plagued by issues such as low identification efficiency and high subjectivity. In order to achieve rapid and accurate identification of diseases that are prevalent in pitaya, this paper focuses on such diseases and proposes a machine-vision detection method based on an improved YOLO11n algorithm. The enhanced architecture incorporates MSCAM for edge-feature enhancement, ACmix to balance local and global perception, and PMHSA to optimize deep semantic flow, collectively addressing the challenges of small-target loss and false positives in pitaya disease detection. The experimental findings demonstrate that the improved model attains a detection accuracy of 95.8%, a recall rate of 92.3%, and an mAP50 of 95.9%. A comparison of the baseline model with the improved model reveals a reduction in the number of parameters representing a decrease of approximately 14.2%. Furthermore, the computational complexity indicates a decline of approximately 3.07%, and a reduction in model size by approximately 4.73%. This approach led to enhanced disease-recognition performance while preserving high levels of accuracy. This study proposes a technically sound solution for the intelligent development of disease-detection models for pitaya.