biostudies-literatureTan ZFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Hubei Provincethe Platform Construction of Genetic Improvement And Molecular Design Breeding For Xinjiang Island CottonNational Natural Science Foundation of China53https://www.ebi.ac.uk/biostudies/studies/S-EPMC9026675biostudies-literatureUnknown18(1)<h4>Background</h4>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.<h4>Result</h4>The single-stage model based on YOLOv5 has Plant methodsFast anther dehiscence status recognition system established by deep learning to screen heat tolerant cotton.PMC90266752021ZKPY0192021CFA0592020172-2U21A202052021ZKPY00632072024Yang JMa YLi QKong JZhang RMin LYang WLi YTan ZZhu LZhang XLv RMa HShi JWu YfalseFast anther dehiscence status recognition system established by deep learning to screen heat tolerant cotton.<h4>Background</h4>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.<h4>Result</h4>The single-stage model based on YOLOv5 has 2022-01-01T00:00:00Z2022 Apr2025-04-22T21:39:39.16Z2024-11-08T09:28:21.3ZS-EPMC90266753544910810.1186/s13007-022-00884-0