基于VFNet-Improved和Deep Sort的棉花黄萎病病情分级

doi:10.12398/j.issn.2096-7217.2023.02.002

摘要/Abstract

摘要： 棉花是全球最重要的经济作物之一，而黄萎病是世界主要棉花生产区的第一大病害，黄萎病病原菌通过感染棉花的根部使叶片萎蔫、褪色以致脱落，导致棉花质量和产量严重下降。国家标准将患黄萎病叶片划分为5个等级，传统检测方法主要依赖人工，存在主观、低效、重复性差等问题，因此提出一种以VFNet-Improved、Deep Sort和撞线匹配机制为主要算法框架的棉花黄萎病病情分级方法，实现在旋转视频输入情况下对患病叶片的数量统计和病情等级的划分。研究首先基于VFNet目标检测网络，融合多尺度训练、动态卷积等优化方法，实现对旋转视频中患病叶片的精准定位；然后采用Deep Sort跟踪器实现前后帧同一叶片的相互关联，并针对跟踪过程ID跳变问题设计了掩膜撞线匹配机制；最后使用OpenCV对经过掩膜线的叶片进行特征提取与患病分级的划分。试验结果表明，VFNet-Improved可以有效改善棉花患病叶片识别精度，mAP75达到0.906，较改进前VFNet模型提升了0.012，帧率FPS为12.9帧/s；Deep Sort跟踪器跟踪效果MOTA为0.835，对患病叶片数量统计结果R²、RMSE、MAE与MAPE分别为0.890、5.138、4.300和14.967%，与人工统计值具有较高一致性。本研究为棉花黄萎病病情精准、高效鉴定提供一种新的科学工具，对棉花抗病品种筛选和遗传机制解析具有重要意义。

关键词: 目标检测, 目标跟踪, VFNet, Deep Sort, 棉花黄萎病, 病情分级

Abstract: Cotton is one of the most important economic crops in the world, and verticillium wilt is the number one disease in major cotton production areas in the world. Verticillium wilt causes the leaves to wilt, fade and even fall off by infecting the roots of cotton, resulting in severe decline both cotton quality and its yield. The national standard divides the leaves suffering from verticillium wilt into five grades. The traditional detection method mainly relies on manual labor, which has problems such as subjectivity, inefficiency, and poor repeatability. The cotton verticillium wilt disease classification method, which is based on VFNet-Improved, Deep Sort, and collision line matching mechanisms as the main algorithm framework, realizes the statistics of the number of diseased leaves and the classification of the disease level under the condition of rotating video input. Based on the VFNet target detection network, the research first combined multi-scale training, dynamic convolution and other optimization methods to achieve accurate positioning of diseased leaves in rotating videos. Then the Deep Sort tracker was used to realize the correlation of the same leaf in the front and back frames, and a mask collision line matching mechanism was designed for the ID jump problem in the tracking process; finally, OpenCV was used to perform feature extraction and disease classification of leaves passing the mask line. The results showed that the VFNet-Improved model could achieve the best effect in the object detection algorithms with 0.906 mAP75, which was 0.012 higher than the VFNet model, and the FPS reached 12.9 frames/s. The tracking result MOTA of the Deep Sort was 0.835. R2, RMSE, MAE and MAPE of VFNet-Improved were 0.890, 5.138, 4.300 and 14.967% respectively, which performed high consistency with manual measurements. In conclusion, this study has demonstrated a novel tool for the accurate and efficient evaluation of cotton verticillium wilt, which would be of great significance for the cotton breeding and genetic analysis research.

Key words: object detection, object tracking, VFNet, Deep Sort, cotton verticillium wilt, ill leaf grading

中图分类号:

S24: TP391.4

黄成龙, 张忠福, 卢智浩, 张晓君, 朱龙付, 杨万能. 基于VFNet-Improved和Deep Sort的棉花黄萎病病情分级[J]. 智能化农业装备学报（中英文）, 2023, 4(2): 12-21.

HUANG Chenglong, ZHANG Zhongfu, LU Zhihao, ZHANG Xiaojun, ZHU Longfu, YANG Wanneng. Leaf grading for cotton verticillium wilt based on VFNet-Improved and Deep Sort[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(2): 12-21.

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