Silage Maize Leaf Blight Detection Model in Field Environment
In order to achieve accurate Detection of Leaf blight of silage Maize,reduce the cost of manual diagnosis in field environment and reduce the impact of the disease,a modified YOLOv7-MLD(Maize Leaf-Blight Detection)model was proposed.Firstly,Diverse Branch Block(DBB)module was added to the backbone of YOLOv7 network to enhance its feature extraction capability.Then a Coordinate Attention module is added to the three output feature layers to enhance the ability of extracting disease features.Finally,the loss function is replaced by CIoU with SIoU to improve the convergence speed and regression accuracy of the bounding box.Experiments were carried out on a subset of maize leaf wilt disease data set,and the results showed that the AP value of YOLOV7-MLD model reached 84.2%,the F1 value increased by 5.9%,the accuracy rate and recall rate increased by 4.3%and 7.3%,respectively,compared with the original YOLOv7.The model can accu-rately locate and identify the leaf blight of silage maize in the complex field environment,and has very important practical significance for guid-ing the prevention and control of the early leaf blight of silage maize.
万方数据
维普
