Authors

Abstract

In response to the automatic extraction of navigation lines for wheat root cutting, this paper conducted field experiments and analyses on the navigation line extraction algorithm, based on the improved YOLOv5 algorithm. Firstly, based on the characteristics of wheat seedling rows during the wheat rejuvenation period, the YOLOv5 algorithm was improved by using rotation detection box labels, and navigation lines were extracted by fitting the detection boxes using clustering methods. Then, an experimental system was established to conduct field experiments on the algorithm: (1) Tests were conducted at three speeds of 0.5 m/s, 1.0 m/s and 1.5 m/s respectively, and the position error of the root cutter was measured and analyzed, indicating that the actual navigation path position error increased with the speed. The best navigation performance was observed at 1 m/s, with an average positional error of 18.56 mm, meeting the requirements for wheat root cutting. (2) Robustness analysis of the algorithm was conducted using data collected from 2019 to 2022. Comparative tests were conducted from four aspects: different years, different time periods, different environments, and different yaw angles. The results showed that the algorithm proposed in this paper has stronger robustness and higher accuracy.

Abstract in Chinese

针对小麦断根导航线自动提取的问题，本文在改进YOLOv5算法的基础上，对导航线提取算法进行了田间实验测试和分析。首先，针对小麦返青期麦苗行的特点，用旋转检测框标识对YOLOv5算法进行改进，通过聚类方法对检测框拟合进行导航线的提取；然后，搭建实验系统对算法进行田间实验：（1）分别在0.5m/s、1.0m/s、1.5m/s三个速度下进行测试，测量分析断根刀的位置误差，表明实际导航路径位置误差随行进速度的增加而变大，1m/s时导航效果最好，位置误差平均值为18.56mm，满足小麦断根要求；（2）利用2019-2022年采集数据对对算法的鲁棒性进行实验分析，从不同年份、不同时段、不同环境及不同偏航角四个方面进行对比测试，结果表明本文算法鲁棒性更强、准确性更高，能够满足作业实时性要求。