文献类型：期刊文献

英文题名：Improving the Recognition of Bamboo Color and Spots Using a Novel YOLO Model

作者：Zhang, Yunlong[1] Nie, Tangjie[2] Zeng, Qingping[2] Chen, Lijie[2] Liu, Wei[1] Zhang, Wei[3] Tong, Long[2]

第一作者：Zhang, Yunlong

通信作者：Tong, L[1]

机构：[1]Zhejiang A&F Univ, Coll Opt Mech & Elect Engn, Hangzhou 311300, Peoples R China;[2]Chongqing Acad Forestry, Chongqing 401147, Peoples R China;[3]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou 311400, Peoples R China

年份：2025

卷号：14

期号：15

外文期刊名：PLANTS-BASEL

收录：;Scopus(收录号：2-s2.0-105013084881);WOS:【SCI-EXPANDED(收录号:WOS:001548782500001)】；

基金：This work was funded by the Zhejiang Forestry Science and Technology Program (2023SY01); the Chongqing Science and Technology Forestry Major Special Project (ZD2022-4); the Key Research Projects of Yibin of Sichuan Province (YBZD2024-1); and the Chongqing Nanchuan District Forestry Bureau Industrial Development Project (HH-90735).

语种：英文

外文关键词：deep learning; bamboo shoots; classification; YOLOv8-BS model; phenotype

摘要：The sheaths of bamboo shoots, characterized by distinct colors and spotting patterns, are key phenotypic markers influencing species classification, market value, and genetic studies. This study introduces YOLOv8-BS, a deep learning model optimized for detecting these traits in Chimonobambusa utilis using a dataset from Jinfo Mountain, China. Enhanced by data augmentation techniques, including translation, flipping, and contrast adjustment, YOLOv8-BS outperformed benchmark models (YOLOv7, YOLOv5, YOLOX, and Faster R-CNN) in color and spot detection. For color detection, it achieved a precision of 85.9%, a recall of 83.4%, an F1-score of 84.6%, and an average precision (AP) of 86.8%. For spot detection, it recorded a precision of 90.1%, a recall of 92.5%, an F1-score of 91.1%, and an AP of 96.1%. These results demonstrate superior accuracy and robustness, enabling precise phenotypic analysis for bamboo germplasm evaluation and genetic diversity studies. YOLOv8-BS supports precision agriculture by providing a scalable tool for sustainable bamboo-based industries. Future improvements could enhance model adaptability for fine-grained varietal differences and real-time applications.