大田环境下青贮玉米枯叶病检测模型

Silage Maize Leaf Blight Detection Model in Field Environment

为实现对青贮玉米枯叶病的精确检测,降低大田环境下的人工诊断成本,减少病害带来的影响,提出一种基于YOLOv7改进的智能检测与识别模型YOLOv7-MLD。首先,在YOLOv7网络的主干中添加DBB模块,增强主干的特征提取能力;其次,在3个输出特征层添加坐标注意力机制(CA)模块,增强对病害特征的提取能力;最后,将损失函数由CIoU替换为SIoU,以提高边界框的收敛速度和回归精度。在玉米枯叶病数据集的子集上进行实验,结果表明,YOLOv7-MLD模型的AP值达到84.2%,与原YOLOv7相比,F1值提高了5.9%,精确率和召回率分别提高了4.3%与7.3%。该模型实现了在田间复杂环境下对青贮玉米枯叶病病害的精准定位与识别,对于指导早期青贮玉米枯叶病病害防治具有十分重要的现实意义。

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 accurately locate and identify the leaf blight of silage maize in the complex field environment,and has very important practical significance for guiding the prevention and control of the early leaf blight of silage maize.