Welcome to Acta Agronomica Sinica，

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

RichHTML438

36

PDF

168

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

Table 1

Seven levels fertility category"

育性等级
Fertility grade		 特征
Trait		 育性分类
Fertility category
0 花药彻底败育, 无花粉或无可育花粉, 花药不能开裂散粉。
The anthers are completely sterile, with no pollen or no fertile pollen, and the anthers cannot dehisce and release pollen.		 0-1级为不育(S)
Levels 0-1 are sterility (S)
1 1-4个雄蕊的花药有微量可育花粉, 但不散粉。
Anthers of 1-4 stamens have trace amounts of fertile pollen but do not shed pollen.
2 5-6个花药有微量可育花粉, 花药膨大部分占正常花药1/3以下, 延迟散粉。
5-6 anthers have trace amounts of fertile pollen, the anther swelling accounts for less than 1/3 of the normal anther, and pollen shedding is delayed.		 2-4级为部分不育(PS)
Levels 2-4 are partial sterility (PS)
3 6个花药有一定数目的可育花粉, 花药膨大部分占正常花药的1/3~1/2。
6 anthers have a certain number of fertile pollen, and the anther swelling accounts for 1/3-1/2 of the normal anther.
4 6个花药有大量可育花粉, 膨大部分占正常花药的1/2-3/4, ♂/♀=4/5。
6 anthers have a large amount of fertile pollen, and the swollen part accounts for 1/2-3/4 of the normal anther, ♂/♀=4/5.
5 6个花药发育正常或基本正常, 花药膨大部分占正常花药的2/3以上, ♂/♀=1。
6 anthers developed normally or almost normally, the anther swelling accounted for more than 2/3 of the normal anther, ♂/♀=1.		 5-6级为可育(F)
Levels 5-6 are fertile (F)
6 6个花药发育正常, ♂/♀>1。
6 anthers developed normally, ♂/♀>1.

Fig. 1

Annotation of rapeseed flower organ images Labelme software was used to label the flower organ images, with stamens labeled as “stamens” and pistils labeled as “stigma”."

Fig. 2

The improved U-Net++architecture"

Table 2

Temperature from 2022 to 2024"

年份(播期)
Years (Sowing date, month/day)		 2月均温
Average temperature
in February (℃)		 3月均温
Average temperature
in March (℃)		 2月负积温
Negative accumulated
temperature in February (℃)		 初花期
Initial flowering
period (month-day)
2021-2022 (10/05) 5.11 15.33 0 03-11
2022-2023 (10/04) 7.75 14.28 0 02-28
2023-2024 (10/08) 5.03 13.63 -8.5 03-04

Fig. 3

Temperature in February and March of 2022-2024"

Table 3

Performance evaluation of classic image segmentation models"

模型
Model		 平均交互比
mIoU (%)		 精确率
P (%)		 召回率
R (%)		 F1分数
F1_score (%)
U-Net++ 82.24 97.60 96.95 97.13
U-Kan 86.91 98.39 98.11 98.19
BRAU-Net++ 86.06 98.19 97.84 97.94
Swin-Unet 81.29 97.57 96.90 97.09
ACC-UNet 86.07 98.01 97.92 98.01
STSU-Net 92.02 98.94 98.84 98.87

Fig. 4

Segmentation performance of different models"

Fig. 5

Interactive interface"

Fig. 6

Correlation between fertility and the ratio of stamen length/pistil length a: relationship between predicted and measured values of phenotype parameters; b: relationship between pollen quantity and the ratio of stamen length/pistil length."

Fig. 7

Fertility distribution of the peak flowering period groups in 2022 and 2023 a-c: frequency distribution of pistil, tetradynamous, and the ratio of stamen/pistil length in 2022. d-f: frequency distribution of pistil, tetradynamous, and the ratio of stamen/pistil length in 2023."

Table 4

Correlation coefficient between stamen/pistil ratio and temperature"

时段
Time		 相关系数
Correlation coefficient		 时段
Time		 相关系数
Correlation coefficient		 时段
Time		 相关系数
Correlation coefficient		 时段
Time		 相关系数
Correlation coefficient
0- -18 0.63 0- -15 0.73 -3- -12 0.02 0- -6 0.76*
-3- -18 0.18 -3- -15 0.14 -6- -12 -0.13 -3- -6 -0.03
-6- -18 -0.18 -6- -15 -0.04 -9- -12 0.43 0- -3 0.35
-9- -18 -0.07 -9- -15 0.19 0- -9 0.79*
-12- -18 0.01 -12- -15 0.44 -3- -9 -0.34
-15- -18 -0.78 0- -12 0.75 -6- -9 0.59
[1] 刘杰, 黄学辉. 作物杂种优势研究现状与展望. 中国科学: 生命科学, 2021, 51: 1396-1404.
Liu J, Huang X H. Advances and perspectives in crop heterosis. Sci Sin Vitae, 2021, 51: 1396-1404 (in Chinese with English abstract).
[2] 袁国强, 陶军, 何员江, 罗江陶, 蒲宗君, 李生荣, 任勇. 杂交小麦研究进展与展望. 四川农业大学学报, 2023, 41: 973-980.
Yuan G Q, Tao J, He Y J, Luo J T, Pu Z J, Li S R, Ren Y. Research progress and prospect on hybrid wheat. J Sichuan Agric Univ, 2023, 41: 973-980 (in Chinese with English abstract).
[3] 汪邑晨, 朱本顺, 周磊, 朱骏, 杨仲南. 光/温敏核不育系的不育机理及两系杂交稻的发展与展望. 中国水稻科学, 2024, 38: 463-474.
doi: 10.16819/j.1001-7216.2024.231008
Wang Y C, Zhu B S, Zhou L, Zhu J, Yang Z N. Sterility mechanism of photoperiod/thermo-sensitive genic male sterile lines and development and prospects of two-line hybrid rice. Chin J Rice Sci, 2024, 38: 463-474 (in Chinese with English abstract).
doi: 10.16819/j.1001-7216.2024.231008
[4] Xiao Q, Wang H D, Chen H, Chen X H, Wen J, Dai C, Ma C Z, Tu J X, Shen J X, Fu T D, et al. Molecular analysis uncovers the mechanism of fertility restoration in temperature-sensitive polima cytoplasmic male-sterile Brassica napus. Int J Mol Sci, 2021, 22: 12450.
[5] 杨光圣.甘蓝型油菜雄性不育的分类和综合利用研究. 华中农业大学博士学位论文, 湖北武汉, 1998.
Yang G S.Study on Classification and Comprehensive Utilization of Male Sterility in Brassica napus L. PhD Dissertation of Huazhong Agricultural University, Wuhan, Hubei, China, 1998 (in Chinese with English abstract).
[6] 袁美.甘蓝型油菜生态型细胞质雄性不育两用系温度调控机理的初步研究. 华中农业大学博士学位论文, 湖北武汉, 2002.
Yuan M.Preliminary Studies on Temperature Regulation on the Ecotypical Male Sterile-fertile Line of Brassica napus L. PhD Dissertation of Huazhong Agricultural University, Wuhan, Hubei, China, 2002 (in Chinese with English abstract).
[7] 杨立勇.Pol TCMS小孢子培养体系的建立与温敏基因的QTL定位. 华中农业大学博士学位论文, 湖北武汉, 2005.
Yang L Y.Establishment of Microspore Culture System for Pol TCMS and QTL Mapping of Its Temperature-sensitive Genes. PhD Dissertation of Huazhong Agricultural University, Wuhan, Hubei, China, 2005 (in Chinese with English abstract).
[8] 肖清.甘蓝型油菜高密度SNP育种芯片的开发及其在Pol-CMS温敏不育研究中的应用. 华中农业大学博士学位论文, 湖北武汉, 2022.
Xiao Q.Development of High-density SNP Breeding Chip and Its Application in Pol-CMS Thermosensitive Sterility Research in Brassica napus. PhD Dissertation of Huazhong Agricultural University, Wuhan, Hubei, China, 2022 (in Chinese with English abstract).
[9] 张小强, 熊博莅, 匡纲要. 一种基于变化检测技术的SAR图像舰船目标鉴别方法. 电子与信息学报, 2015, 37(1): 63-70.
Zhang X Q, Xiong B L, Kuang G Y. A ship target discrimination method based on change detection in SAR imagery. J Electr Inf Technol, 2015, 37(1): 63-70 (in Chinese with English abstract).
[10] 徐志祥, 孙文博, 邢立东, 高东, 赵炎. 基于改进Canny边缘检测的堆叠货箱分割定位方法. 测控技术, 2024, 43(3): 28-33.
Xu Z X, Sun W B, Xing L D, Gao D, Zhao Y. Segmentation and location method of stacked containers based on improved canny edge detection. Meas Contr Technol, 2024, 43(3): 28-33 (in Chinese with English abstract).
[11] Zeng Y Z, Zhao Y Q, Liao S H, Liao M, Chen Y, Liu X Y. Liver vessel segmentation based on centerline constraint and intensity model. Biomed Signal Process Contr, 2018, 45: 192-201.
[12] Fukushima K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36: 193-202.
doi: 10.1007/BF00344251 pmid: 7370364
[13] Ronneberger O, Fischer P, Brox T. U-Net:convolutional networks for biomedical image segmentation. In: RonnebergerO, FischerP, BroxT, Medical Image Computing and Computer- assisted Intervention-MICCAI 2015.eds. Cham: Springer International Publishing, 2015. pp 234-241.
[14] Long J, Shelhamer E, Darrell T.Fully convolutional networks for semantic segmentation. In: LongJ, ShelhamerE, DarrellT, eds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Computer Vision Foundation, 2015. pp 3431-3440.
[15] 刘林, 林山驰, 李相国, 冯敏, 许亮. 基于改进的U-Net网络模型的叶片病害检测. 液晶与显示, 2024, 39: 1138-1144.
Liu L, Lin S C, Li X G, Feng M, Xu L. Leaf disease detection based on improved U-Net network model. Chin J Liquid Crystal Displays, 2024, 39: 1138-1144 (in Chinese with English abstract).
[16] 洪俐, 刘涛, 孙成明, 左示敏, 严长杰. 基于改进U-Net++的水稻病害图像分割研究. 江苏农业科学, 2024, 52(12): 201-208.
Hong L, Liu T, Sun C M, Zuo S M, Yan C J. Study on rice disease image segmentation based on improved U-NET ++ model. Jiangsu Agric Sci, 2024, 52(12): 201-208 (in Chinese with English abstract).
[17] Zhou Z W, Tajbakhsh N, Liang J M.Rahman Siddiquee M M, UNet++:a nested U-Net architecture for medical image segmentation. In: ZhouZ W, RahmanSiddiquee M M, TajbakhshN, LiangJ M, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.eds. Cham: Springer International Publishing, 2018. pp 3-11.
[18] Wang G W, Wang J W, Wang J X, Sun Y D. Grape leaf disease classification combined with U-net++ network and threshold segmentation. Comput Intell Neurosci, 2022, 2022: 1042737.
[19] Xiong X, Yu L J, Yang W N, Liu M, Jiang N, Wu D, Chen G X, Xiong L Z, Liu K D, Liu Q. A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage. Plant Methods, 2017, 13: 7.
doi: 10.1186/s13007-017-0157-7 pmid: 28163771
[20] Liang X Y, Xu X C, Wang Z W, He L, Zhang K Q, Liang B, Ye J L, Shi J W, Wu X, Dai M Q, et al. StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model. Plant Biotechnol J, 2022, 20: 577-591.
[21] Wu W, Liu T, Zhou P, Yang T L, Li C Y, Zhong X C, Sun C M, Liu S P, Guo W S. Image analysis-based recognition and quantification of grain number per panicle in rice. Plant Methods, 2019, 15: 122.
doi: 10.1186/s13007-019-0510-0 pmid: 31695727
[22] 梁丽秀.基于MATLAB的水稻表层根系图像分割算法研究. 华中农业大学硕士学位论文, 湖北武汉, 2018.
Liang L X. Research on Image Segmentation Algorithm of Rice Surface Root System Based on MATLAB. MS Thesis of Huazhong Agricultural University, Wuhan, Hubei, China, 2018 (in Chinese with English abstract).
[23] Wu X, Feng H, Wu D, Yan S J, Zhang P, Wang W B, Zhang J, Ye J L, Dai G X, Fan Y, et al. Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance. Genome Biol, 2021, 22: 185.
doi: 10.1186/s13059-021-02377-0 pmid: 34162419
[24] Hu J, Shen L, Albanie S, Sun G, Wu E H. Squeeze-and-excitation networks. In: HuJ, ShenL, AlbanieS, SunG, WuE H, eds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Computer Vision Foundation, 2018. pp 7132-7141.
[25] Misra D, Nalamada T, Arasanipalai A U, Hou Q B.Rotate to attend: convolutional triplet attention module. In: Misra D, Nalamada T, Arasanipalai A U, Hou Q B, eds.Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Computer Vision Foundation, 2021. pp 3139-3148.
[26] Yu T, Li X, Cai Y F, Sun M M, Li P. S2-MLPv2: improved spatial-shift MLP architecture for vision. Tex: Cornell University, 2021 [2021-08-02].
[27] 温静. 通过广度优先搜索有向图重组规则次序. 湖北大学学报(自然科学版), 2004, 26(4): 294-296.
Wen J. Reordering rule sequences by breadth-first searching directed graph. J Hubei Univ (Nat Sci Edn), 2004, 26(4): 294-296 (in Chinese with English abstract).
[28] 刘云飞, 张俊然. 深度神经网络学习率策略研究进展. 控制与决策, 2023, 38: 2444-2460.
Liu Y F, Zhang J R. Research advances in deep neural networks learning rate strategies. Control Decision, 2023, 38: 2444-2460 (in Chinese with English absract).
[29] 蒋文斌, 彭晶, 叶阁焰. 深度学习自适应学习率算法研究. 华中科技大学学报(自然科学版), 2019, 47(5): 79-83.
Jiang W B, Peng J, Ye G Y. Research on adaptive learning rate algorithm in deep learning. J Huazhong Univ Sci Technol (Nat Sci Edn), 2019, 47(5): 79-83 (in Chinese with English abstract).
[30] Berman M, Triki A R, Blaschko M B. The lovasz-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: BermanM, TrikiA R, BlaschkoM B, eds. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Computer Vision Foundation, 2018. pp 4413-4421.
[31] 谢俊, 杨超宇. 基于改进YOLOv5s的高压环境穿戴绝缘用具检测算法. 重庆科技学院学报(自然科学版), 2023, 25(6): 59-64.
Xie J, Yang C Y. Study on algorithm for detecting insulated tools in high voltage environments based on YOLOv5s. J Chongqing Univ Sci Technol (Nat Sci Edn), 2023, 25(6): 59-64 (in Chinese with English abstract).
[32] Li C X, Liu X Y, Li W Y, Wang C, Liu H Y, Liu Y F, Chen Z, Yuan Y X. U-KAN makes strong backbone for medical image segmentation and generation. Tex: Cornell University, 2024 [2024-08-22].
[33] Lan L B, Cai P Z, Jiang L, Liu X J, Li Y M, Zhang Y D. BRAU-net++: U-shaped hybrid CNN-transformer network for medical image segmentation. Tex: Cornell University, 2024 [2024-09-30].
[34] Cao H, Wang Y Y, Chen J, Jiang D S, Zhang X P, Tian Q, Wang M N. Swin-unet:unet-like pure transformer for medical image segmentation. In: CaoH, WangY Y, ChenJ, JiangD S, ZhangX P, TianQ, WangM N, EuropeanConference on Computer Vision.eds. Cham: Springer Nature Switzerland, 2023. pp 205-218.
[35] Ibtehaz N, Kihara D. ACC-UNet: a completely convolutional UNet model for the 2020s. In: Ibtehaz N, Kihara D, eds. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023. pp 692-702.
[36] Minervini M, Fischbach A, Scharr H, Tsaftaris S A. Finely- grained annotated datasets for image-based plant phenotyping. Pattern Recognit Lett, 2016, 81: 80-89.
[37] Zhang X H, Huang C L, Wu D, Qiao F, Li W Q, Duan L F, Wang K, Xiao Y J, Chen G X, Liu Q, et al. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol, 2017, 173: 1554-1564.
doi: 10.1104/pp.16.01516 pmid: 28153923
[38] 赵亚凤, 刘晓璐, 王冬冬, 王孟雪, 宋文华, 胡峻峰. 基于改进U-Net的根系表型参数测量系统. 森林工程, 2024, 40(4): 127-136.
Zhao Y F, Liu X L, Wang D D, Wang M X, Song W H, Hu J F. Root phenotypic parameter measurement system based on improved U-Net. For Engin, 2024, 40(4): 127-136 (in Chinese with English abstract).
[39] 陈才武.甘蓝型油菜pol TCMS温度感受器基因突变体的创制. 华中农业大学硕士学位论文, 湖北武汉, 2023.
Chen C W. Creation of Thermoreceptor Genes Mutants for pol TCMS in Brassica napus L. MS Thesis of Huazhong Agricultural University, Wuhan, Hubei, China, 2023 (in Chinese with English abstract).
[1] CUI Xin, GU He-He, SONG Yi, ZHANG Zhe, LIU Shi-Shi, LU Zhi-Feng, REN Tao, LU Jian-Wei. Effects of potassium fertilizer application rates on rapeseed yield and potassium absorption and yield reduction caused by frost damage [J]. Acta Agronomica Sinica, 2025, 51(6): 1629-1642.
[2] ZHAO Gang, ZHANG Jian-Jun, DANG Yi, FAN Ting-Lu, WANG Lei, ZHOU Gang, WANG Shu-Ying, LI Xing-Mao, NI Sheng-Li, MI Wen-Bo, ZHOU Xu-Jiao, CHENG Wan-Li, LI Shang-Zhong. Effects of straw mulching on soil water temperature effect and winter wheat yield in different rainfall years in Dryland Loess Plateau [J]. Acta Agronomica Sinica, 2025, 51(6): 1643-1653.
[3] WANG Meng-Ning, XIE Ke-Ran, GAO Ti, WANG Fei, REN Xiao-Jian, XIONG Dong-Liang, HUANG Jian-Liang, PENG Shao-Bing, CUI Ke-Hui. Effect of high temperature during the panicle initiation and heading stages on grain shape and filling and its relationship with grain weight in rice [J]. Acta Agronomica Sinica, 2025, 51(5): 1347-1362.
[4] WANG Jia-Jie, WANG Zheng-Nan, BATOOL Maria, WANG Wang-Nian, WEN Jing, REN Chang-Zhong, HE Feng, WU You-You, XU Zheng-Hua, WANG Jing, KUAI Jie, WANG Bo, ZHOU Guang-Sheng, FU Ting-Dong. Comparison of physiological characteristics of salt and alkali tolerance between rapeseed and wheat [J]. Acta Agronomica Sinica, 2025, 51(5): 1215-1229.
[5] MENG Fan-Qi, FANG Meng-Ying, LUO Yi, LU Lin, DONG Xue-Rui, WANG Ya-Fei, GUO Li-Na, YAN Peng, DONG Zhi-Qiang, ZHANG Feng-Lu. Effect of ethephon betaine salicylic acid mixture on heat resistance and yield of summer maize [J]. Acta Agronomica Sinica, 2025, 51(5): 1299-1311.
[6] LI Pei-Hua, LI Jie, MENG Xiang-Yu, SUN Yu-Chen, FENG Yong-Jia, LI Yun-Li, DIAO Deng-Chao, ZHAO Wen, WU Wei, HAN De-Jun, ZHANG Song-Wu, ZHENG Wei-Jun. Evaluation of stress tolerance and physiological response of cold-type wheat under heat stress [J]. Acta Agronomica Sinica, 2025, 51(4): 1118-1130.
[7] LI Qiao, YE Yang-Chun, CHANG Xu-Hong, WANG De-Mei, WANG Yan-Jie, YANG Yu-Shuang, MA Rui-Qi, ZHAO Guang-Cai, CAI Rui-Guo, ZHANG Min, LIU Xi-Wei. Effects of high temperature and drought stresses on photosynthetic characteristics and yield of winter wheat after anthesis [J]. Acta Agronomica Sinica, 2025, 51(4): 1077-1090.
[8] WANG Jiao, BAI Hai-Xia, HAN Yu-Yan, LIANG Hui, FENG Ya-Nan, ZHANG Dong-Sheng, LI Ping, ZONG Yu-Zheng, SHI Xin-Rui, HAO Xing-Yu. Effects of elevated CO2 concentration, increased temperature and their interaction on the carbon and nitrogen metabolism in Liangxing 99 winter wheat leaves [J]. Acta Agronomica Sinica, 2025, 51(4): 1061-1076.
[9] SHE Hui-Jie, SUN Ming-Zhu, LI Shi-Gang, WANG Dong-Xian, CHENG Tai, JIANG Bo, CHEN Ai-Wu, WANG Jing, ZHAO Jie, WANG Bo, KUAI Jie, XU Zheng-Hua, ZHOU Guang-Sheng. Effect of coating with fulvic acid and alginate oligosaccharide on emergence and yield of late-sown rapeseed [J]. Acta Agronomica Sinica, 2025, 51(4): 1022-1036.
[10] HOU Tian-Yu, DU Xiao-Jing, ZHAO Zhi-Qiang, REYIM Anwar, YIDAYETULA Abula, BUHALIQIEMU Abulizi, YUAN Jie, ZHANG Yan-Hong, WANG Feng-Bin. Evaluation of cold tolerance of japonica rice varieties at germination stage and construction of identification system [J]. Acta Agronomica Sinica, 2025, 51(3): 812-822.
[11] SU Qing-Fang, SUN Xiao-Zhao, LIN Yang, FU Yan-Ping, CHENG Jia-Sen, XIE Jia-Tao, JIANG Dao-Hong, CHEN Tao. Serratia nematodiphila TG10 enhanced salt-alkali tolerance in rapeseed [J]. Acta Agronomica Sinica, 2025, 51(2): 358-369.
[12] HU Ya-Jie, GUO Jing-Hao, CONG Shu-Min, CAI Qin, XU Yi, SUN Liang, GUO Bao-Wei, XING Zhi-Peng, YANG Wen-Fei, ZHANG Hong-Cheng. Effect of low temperature and weak light stress during early grain filling on rice yield and quality [J]. Acta Agronomica Sinica, 2025, 51(2): 405-417.
[13] XIN Yu-Ning, REN Hao, WANG Hong-Zhang, LIANG Ming-Lei, YU Tao, LIU Peng. Effects of spraying 6-benzylaminopurine (6-BA) on grain filling and yield of summer maize under post-pollination high temperature stress [J]. Acta Agronomica Sinica, 2025, 51(2): 418-431.
[14] QIN Meng-Qian, HUANG Wei, CHEN Min, NING Ning, HE De-Zhi, HU Bing, XIA Qi-Xin, JIANG Bo, CHENG Tai, CHANG Hai-Bin, WANG Jing, ZHAO Jie, WANG Bo, KUAI Jie, XU Zheng-Hua, ZHOU Guang-Sheng. Effect of nitrogen fertilizer management on yield and resistance of late-seeded rapeseed [J]. Acta Agronomica Sinica, 2025, 51(2): 432-446.
[15] WANG Chong-Ming, LU Zhi-Feng, YAN Jin-Yao, SONG Yi, WANG Kun-Kun, FANG Ya-Ting, LI Xiao-Kun, REN Tao, CONG Ri-Huan, LU Jian-Wei. Effect of phosphorus fertilizer rates on crop yield, phosphorus uptake and its stability in rapeseed-rice rotation system [J]. Acta Agronomica Sinica, 2025, 51(2): 447-458.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Li Shaoqing, Li Yangsheng, Wu Fushun, Liao Jianglin, Li Damo. Optimum Fertilization and Its Corresponding Mechanism under Complete Submergence at Booting Stage in Rice[J]. Acta Agronomica Sinica, 2002, 28(01): 115 -120 .
[2] Wang Lanzhen;Mi Guohua;Chen Fanjun;Zhang Fusuo. Response to Phosphorus Deficiency of Two Winter Wheat Cultivars with Different Yield Components[J]. Acta Agron Sin, 2003, 29(06): 867 -870 .
[3] YANG Jian-Chang;ZHANG Jian-Hua;WANG Zhi-Qin;ZH0U Qing-Sen. Changes in Contents of Polyamines in the Flag Leaf and Their Relationship with Drought-resistance of Rice Cultivars under Water Deficiency Stress[J]. Acta Agron Sin, 2004, 30(11): 1069 -1075 .
[4] Yan Mei;Yang Guangsheng;Fu Tingdong;Yan Hongyan. Studies on the Ecotypical Male Sterile-fertile Line of Brassica napus L.Ⅲ. Sensitivity to Temperature of 8-8112AB and Its Inheritance[J]. Acta Agron Sin, 2003, 29(03): 330 -335 .
[5] Wang Yongsheng;Wang Jing;Duan Jingya;Wang Jinfa;Liu Liangshi. Isolation and Genetic Research of a Dwarf Tiilering Mutant Rice[J]. Acta Agron Sin, 2002, 28(02): 235 -239 .
[6] WANG Li-Yan;ZHAO Ke-Fu. Some Physiological Response of Zea mays under Salt-stress[J]. Acta Agron Sin, 2005, 31(02): 264 -268 .
[7] TIAN Meng-Liang;HUNAG Yu-Bi;TAN Gong-Xie;LIU Yong-Jian;RONG Ting-Zhao. Sequence Polymorphism of waxy Genes in Landraces of Waxy Maize from Southwest China[J]. Acta Agron Sin, 2008, 34(05): 729 -736 .
[8] HU Xi-Yuan;LI Jian-Ping;SONG Xi-Fang. Efficiency of Spatial Statistical Analysis in Superior Genotype Selection of Plant Breeding[J]. Acta Agron Sin, 2008, 34(03): 412 -417 .
[9] WANG Yan;QIU Li-Ming;XIE Wen-Juan;HUANG Wei;YE Feng;ZHANG Fu-Chun;MA Ji. Cold Tolerance of Transgenic Tobacco Carrying Gene Encoding Insect Antifreeze Protein[J]. Acta Agron Sin, 2008, 34(03): 397 -402 .
[10] ZHENG Xi;WU Jian-Guo;LOU Xiang-Yang;XU Hai-Ming;SHI Chun-Hai. Mapping and Analysis of QTLs on Maternal and Endosperm Genomes for Histidine and Arginine in Rice (Oryza sativa L.) across Environments[J]. Acta Agron Sin, 2008, 34(03): 369 -375 .
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd