TTLD-YOLOv7:非结构化环境下茶树病害的检测算法OA北大核心CSTPCD

Tea diseases have an extremely serious impact on tea plantations and related industries.Traditional methods for disease detection in the dynamic and complex tea plantation environment are inefficient and unsatisfactory.This study proposed that a YOLOv7-tiny-based model enhanced the fine-grained detection of tea tree diseases.By integrating CoordConv and ECA channel attention mechanisms,this model achieved higher spatial recognition capability in convolutional feature maps and reduced the effect of background noise on feature recognition.Further improvements included the use of a normalized Wasserstein distance metric and decoupled heads to improve the detection of small spots.A new anchor frame was generated using the K-means algorithm based on the specificity of tea spots to improve the accuracy and generalizability of the model.Comparative analysis shows that the model outperforms the existing models Faster R-CNN,SSD,YOLOv5s,YOLO-Tea,YOLOv7-tiny,and YOLOv7,with an average accuracy improvement of 5.9 percentage points to 93%.The improved model could be applied to tea disease monitoring.