Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases

Tea diseases, including brown and gray blight, result in significant yield and quality losses, especially in Longjing tea production. Traditional detection methods are prone to errors, while existing deep learning models often struggle to be robust under natural field conditions. To address these challenges, an improved lightweight detection model, asymmetric multi-level (AML) mechanism, dynamic snake convolution (DSC), and scalable intersection over union (SIoU) loss function-You Only Look Once (YOLO) (ADS-YOLO), was developed and validated. In the method, a dataset comprising 5694 smartphone-captured images of tea leaves was established under natural lighting. Enhancements were implemented in the YOLO11n baseline algorithm through incorporation of the SIoU loss function for better bounding box regression, DSC, which realizes adaptive feature extraction based on the dynamic spatial context, and an AML mechanism, which achieves lightweight feature fusion via adaptive multi-scale design. The results showed that ADS-YOLO achieved a precision of 0.935 and a recall of 0.870, compared to 0.894 and 0.818, respectively, when the baseline YOLO11n was used. Importantly, ADS-YOLO demonstrated a real-time performance of 137.1 frames per second (FPS), coupled with reduced computational costs. ADS-YOLO improved the mean average precision (mAP) at intersection over union threshold of 0.5 (mAP@0.5) by 6.4% compared with YOLOv5n and achieved up to 44.6% higher accuracy than YOLOv7t. In conclusion, ADS-YOLO achieved high accuracy, providing a scalable solution for real-time crop health monitoring and sustainable precision agriculture for tea production.