基于YOLOv3的多类烟草叶部病害检测研究

烟草叶部病害种类繁多，病理复杂，严重影响烟草产量及品质，烟草病害精准检测是烟草病害及时防治的前提。传统检测方式精准性差、效率低，基于深度学习的算法可提高烟草病害检测准确性。本文以5种较为常见的烟草病害（普通花叶病、黄瓜花叶病毒病、赤星病、烟草野火病、气候性斑点病）为研究对象，构建基于YOLOv3的烟草病害检测模型，实现烟草多类病害的精准快速检测。使用Darknet53特征网络提取烟叶病害特征并将不同尺度病害特征融合，并用K-means++算法对融合后特征进行分类和位置预测，通过非极大值抑制算法（NMS）去除冗余框，得到病害区域预测框。用田间实际采集的烟草病害数据集，对构建的YOLOv3病害检测模型与SSD（Single Shot multibox Detector）模型对比测试。结果表明，YOLOv3的mIoU为0.81，明显优于SSD的0.73，且YOLOv3模型的mAP为0.77，也高于SSD的0.69。本研究构建的YOLOv3烟草病害检测模型能有效定位烟叶病害区域，实现多类烟草病害的检测，为精准病害防治提供参考。

Detection of Various Tobacco Leaf Diseases Based on YOLOv3

There are many kinds of tobacco leaf diseases with complex pathology, which seriously affect yield and quality of tobacco. Accurate detection of tobacco diseases is prerequisite for timely prevention and control of tobacco diseases. Traditional detection methods have poor accuracy and low efficiency, and algorithms based on deep learning can improve accuracy of tobacco disease detection. In this study, five common tobacco diseases (common mosaic disease, cucumber mosaic virus disease, scab disease, tobacco wildfire disease, and climatic spot disease) were taken as research objects, and a tobacco disease detection model based on YOLOv3 was constructed to achieve accurate and rapid detection a variety of tobacco diseases. Darknet53 feature network was used to extract tobacco leaf disease features and fuse disease features of different scales. K-means++ algorithm was used to classify and position the fused features, and non-maximum suppression algorithm (NMS) was used to remove redundant frames to obtain disease area predictions frame. Using the tobacco disease data set collected in the field, the constructed YOLOv3 disease detection model and the SSD (Single Shot multibox Detector) model were compared and tested. The results showed that YOLOv3's mIoU of 0.81 was significantly better than SSD's 0.73, and the YOLOv3 model's mAP of 0.77 was also higher than SSD's 0.69. The YOLOv3 tobacco disease detection model constructed in this study can effectively locate the disease area of tobacco leaves, realize the detection of various types of tobacco diseases, and provide a reference for precise disease control.