文献类型： 外文期刊
作者： Hu, Jinnan 1 ; Li, Guo 1 ; Mo, Haolan 2 ; Lv, Yibo 1 ; Qian, Tingting 3 ; Chen, Ming 1 ; Lu, Shenglian 1 ;
作者机构： 1.Guangxi Normal Univ, Coll Comp Sci & Engn, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China
2.Guilin Ctr Agr Sci & Technol Res, Guilin 541004, Peoples R China
3.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, Shanghai 201403, Peoples R China
关键词： plant phenotyping; node detection; internode length; YOLOv5
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）
ISSN：
年卷期： 2023 年 13 卷 2 期
页码：
收录情况： SCI
摘要： The extraction and analysis of plant phenotypic characteristics are critical issues for many precision agriculture applications. An improved YOLOv5 model was proposed in this study for accurate node detection and internode length estimation of crops by using an end-to-end approach. In this improved YOLOv5, a feature extraction module was added in front of each detection head, and the bounding box loss function used in the original network of YOLOv5 was replaced by the SIoU bounding box loss function. The results of the experiments on three different crops (chili, eggplant, and tomato) showed that the improved YOLOv5 reached 90.5% AP (average precision) and the average detection time was 0.019 s per image. The average error of the internode length estimation was 41.3 pixels, and the relative error was 7.36%. Compared with the original YOLOv5, the improved YOLOv5 had an average error reduction of 5.84 pixels and a relative error reduction of 1.61%.
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