Xu Jie, Yao Jia, Zhai Hang, Li Qimeng, Xu Qi, Xiang Ying, Liu Yaxi, Liu Tianhong, Ma Huili, Mao Yan, Wu Fengkai, Wang Qingjun, Feng Xuanjun, Mu Jiong, Lu Yanli
Maize Research Institute, Sichuan Agricultural University, Wenjiang 611130, Sichuan, China.
State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang 611130, Sichuan, China.
Plant Phenomics. 2023;5:0024. doi: 10.34133/plantphenomics.0024. Epub 2023 Feb 28.
Plant trichomes are epidermal structures with a wide variety of functions in plant development and stress responses. Although the functional importance of trichomes has been realized, the tedious and time-consuming manual phenotyping process greatly limits the research progress of trichome gene cloning. Currently, there are no fully automated methods for identifying maize trichomes. We introduce TrichomeYOLO, an automated trichome counting and measuring method that uses a deep convolutional neural network, to identify the density and length of maize trichomes from scanning electron microscopy images. Our network achieved 92.1% identification accuracy on scanning electron microscopy micrographs of maize leaves, which is much better performed than the other 5 currently mainstream object detection models, Faster R-CNN, YOLOv3, YOLOv5, DETR, and Cascade R-CNN. We applied TrichomeYOLO to investigate trichome variations in a natural population of maize and achieved robust trichome identification. Our method and the pretrained model are open access in Github (https://github.com/yaober/trichomecounter). We believe TrichomeYOLO will help make efficient trichome identification and help facilitate researches on maize trichomes.
植物毛状体是表皮结构，在植物发育和应激反应中具有多种功能。尽管毛状体的功能重要性已得到认识，但繁琐且耗时的手动表型分析过程极大地限制了毛状体基因克隆的研究进展。目前，尚无用于鉴定玉米毛状体的全自动方法。我们引入了TrichomeYOLO，这是一种使用深度卷积神经网络的毛状体自动计数和测量方法，用于从扫描电子显微镜图像中识别玉米毛状体的密度和长度。我们的网络在玉米叶片的扫描电子显微镜显微照片上实现了92.1%的识别准确率，其表现远优于其他5种当前主流的目标检测模型，即Faster R-CNN、YOLOv3、YOLOv5、DETR和Cascade R-CNN。我们应用TrichomeYOLO研究了玉米自然群体中的毛状体变异，并实现了可靠的毛状体识别。我们的方法和预训练模型在Github（https://github.com/yaober/trichomecounter）上开放获取。我们相信TrichomeYOLO将有助于实现高效的毛状体识别，并促进对玉米毛状体的研究。
