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Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (6): 1423-1434.doi: 10.3724/SP.J.1006.2025.44198

• CROP GENETICS & BREEDING · GERMPLASM RESOURCES · MOLECULAR GENETICS •     Next Articles

Identification of fertility levels and quantification of the temperature-fertility relationship in rapeseed pol TCMS lines using an improved U-Net++ model

LI Shi-Peng1,CHEN Cai-Wu2,ZHANG Jing1,LYU Tian1,FU Ting-Dong1,YI Bin1,*   

  1. 1 College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China; 2 Guangdong Shaoguan Tobacco Redrying Co., Ltd., Shaoguan 512023, Guangdong, China
  • Received:2024-12-01 Revised:2025-03-26 Accepted:2025-03-26 Online:2025-06-12 Published:2025-04-10
  • Supported by:
    This study was supported by the National Key Research and Development Program of China (2021YFD1600500) and the Wuhan Science and Technology Major Project on Key Techniques of Biological Breeding and Breeding of New Varieties (2022021302024851).

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Abstract:

Rapeseed (Brassica napus L.) is a globally important oil crop, and the exploitation of its hybrid vigor has played a crucial role in enhancing yield and environmental adaptability. The thermosensitive Polima cytoplasmic male sterile (pol TCMS) line has a unique dual-purpose advantage, as its fertility is temperature-dependent, making it highly valuable for two-line breeding systems. In agricultural practice, the classification of fertility status is primarily based on the relative lengths of stamens and pistils. However, traditional fertility grading methods rely on subjective assessment, which is prone to human error, ultimately affecting phenotypic investigations and precise trait mapping. To address this issue, we propose a novel approach for fertility classification based on image semantic segmentation using an improved U-Net++ deep learning model. An F2 segregating population was developed using the pol TCMS thermosensitive line and the pol CMS stable sterile line. First, floral organ images representing different fertility levels within the segregating population were collected and annotated to construct a dataset. Next, we optimized the U-Net++ model by refining its encoder-decoder architecture and integrating a channel attention module to enhance segmentation accuracy. Finally, the model was trained and tested on images corresponding to different fertility levels. Experimental results demonstrated that the improved model achieved a mean intersection-over-union (mIoU) of 92.02%, precision of 98.94%, recall of 98.84%, and an F1 score of 98.87%, outperforming other segmentation models. The model effectively distinguished floral organs across different fertility grades and enabled in situ measurements of organ length based on segmentation results. Compared with manual measurements, the predicted values exhibited a coefficient of determination (R2) of 0.989, a root mean square error (RMSE) of 0.142 mm, and a Spearman correlation coefficient of 0.983, demonstrating high accuracy in phenotypic parameter estimation across different fertility levels. Furthermore, we quantitatively analyzed the relationship between temperature and fertility (stamen/pistil ratio), revealing that fertility fluctuates in response to temperature variations during the nine days preceding anthesis in individual flowers. This study confirms the key role of temperature in regulating fertility and provides a novel methodological framework and technical support for in-depth analyses of temperature-sensitive traits and gene mapping in rapeseed.

Key words: rapeseed, Pol TCMS, U-Net++ segmentation model, temperature, fertility grade, gene location

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