文献类型： 外文期刊

作者： Tan, Zhihao ¹ ; Shi, Jiawei ¹ ; Lv, Rongjie ¹ ; Li, Qingyuan ² ; Yang, Jing ³ ; Ma, Yizan ¹ ; Li, Yanlong ¹ ; Wu, Yuanlong ¹ ; Zhang, Rui ¹ ; Ma, Huanhuan ¹ ; Li, Yawei ¹ ; Zhu, Li ¹ ; Zhu, Longfu ¹ ; Zhang, Xianlong ¹ ; Kong, Jie ³ ; Yang, Wanneng ¹ ; Min, Ling ¹ ;

作者机构： 1.Huazhong Agr Univ, Natl Key Lab Crop Genet Improvement, Wuhan 430070, Hubei, Peoples R China

2.Wuhan Acad Agr Sci, Forestry & Fruit Tree Res Inst, Wuhan 430075, Peoples R China

3.Xinjiang Acad Agr Sci, Inst Econ Crops, Urumqi 830091, Xinjiang, Peoples R China

关键词： Cotton anther; Deep learning; Faster R-CNN; YOLOv5; Model ensemble; High temperature stress

期刊名称：PLANT METHODS （ 影响因子：5.827； 五年影响因子：5.904 ）

ISSN：

年卷期： 2022 年 18 卷 1 期

页码：

收录情况： SCI

摘要： Background From an economic perspective, cotton is one of the most important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. Anther dehiscence or indehiscence directly determines the probability of fertilization in cotton. Thus, rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. Result The single-stage model based on YOLOv5 has higher recognition speed and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies are proposed for the Faster R-CNN model, where the improved model has higher detection accuracy than the YOLOv5 model. We have made three improvements to the Faster R-CNN model and after the ensemble of the three models and original Faster R-CNN model, R-2 of "open" reaches to 0.8765, R-2 of "close" reaches to 0.8539, R-2 of "all" reaches to 0.8481, higher than the prediction results of either model alone, which are completely able to replace the manual counting results. We can use this model to quickly extract the dehiscence rate of cotton anthers under high temperature (HT) conditions. In addition, the percentage of dehiscent anthers of 30 randomly selected cotton varieties were observed from the cotton population under normal conditions and HT conditions through the ensemble of the Faster R-CNN model and manual counting. The results show that HT decreased the percentage of dehiscent anthers in different cotton lines, consistent with the manual method. Conclusions Deep learning technology have been applied to cotton anther dehiscence status recognition instead of manual methods for the first time to quickly screen HT-tolerant cotton varieties. Deep learning can help to explore the key genetic improvement genes in the future, promoting cotton breeding and improvement.