1989.9-1993.7 莱阳农学院作物专业，学士

1997.9-2000.7 中国农业大学作物遗传育种专业，硕士

2002.4-2008.12 中国农业大学农学院，讲师

2005.9-2008.12 中国农业大学种子科学专业，博士

2008.12至今   中国农业大学农学院，副教授，博导

主讲课程：种子加工与贮藏；种科专业实习（1）、（2）；种子创艺

主编教材：种子加工与贮藏（第二版）（2016，北京：高教出版社，2012年获批北京市高等教育精品教材）

副主编教材：种子学 （2021，北京：科技出版社）

                    种子加工贮藏与检验实验教程(2019, 北京：科学出版社)

                    种子活力测定技术手册(3本)（2018，北京：中国农业大学出版社）

参编教材：种子生产学（2021，北京：高教出版社）

                 河西地区杂交玉米种子生产技术培训手册（2018，北京：中国农业大学出版社） 

                 现代种子种苗实验指南（2015，北京：中国农业出版社）

教育部高等学校种子科学与工程专业教学指导分委员会 秘书长

1．种子质量无损检测

2．种子加工、贮藏

1、“十三五”国家重点研发计划

2、中医药局项目中药材种子加工项目

3、公益性行业(农业)科研专项

4、北京市科委项目

5、新疆生产建设兵团十二五规划项目

6、农业部种子全程技术研究北京创新中心建设项目

1. Keling Tu, Yulin Yin, Liming Yang, Jianhua Wang, Qun Sun*. Discrimination of individual seed viability by using the oxygen consumption technique and headspace-gas chromatography-ion mobility spectrometry, Journal of Integrative Agriculture, 2023，22(3): 727-737

2.Keling Tu, Weifeng Wu, Ying Cheng, Han Zhang, Yanan Xu, Xuehui Dong，Mang Wang*, Qun Sun*. AIseed: Automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual seeds.  Computers and Electronics in Agriculture，2023，207：107740

3.Keling Tu, Shaozhe Wen, Tong Pan, Ying Cheng, Yanan Xu, Haonan Hou, Jialiang Liu, Riliang Gu, Jianhua Wang, Fengge Wang*, Qun Sun*. A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning. Plant Methods, 2022，18: 81  DOI: 10.1186/s13007-022-00918-7

4. Han Zhang, Qiling Hou, Bin Luo, Keling Tu, Changping Zhao*, Qun Sun*. A method for seed purity detection of hybrid wheat based on transmission hyperspectral imaging technology. Frontiers in Plant Sciences. 2022，DOI: 10.3389/fpls.2022.1015891

5. Tu KL，Cheng Y, Pan T, Wang JH, Sun Q *. Effects of seed priming on vitality and preservation of pepper seeds. Agriculture, 2022，DOI:10.3390/agriculture12050603

6.程莹，许亚男，侯浩楠，杨成民，宁翠玲，董学会，曹海禄*，孙群*. 基于机器视觉的小粒中药材种子净度快速检测研究. 中国农业大学学报，2022，27(5):114-122

7.潘同, 吴伟锋, 侯浩楠, 许亚男, 涂柯玲, 王建华, 孙群*. 机器视觉技术对氯化三苯基四氮唑染色法鉴定玉米种子生活力的改进, 中国农业大学学报，2022，27(5):106-113

8. Tingting Zhang, A. Charfedinne, Ian D. Fisk, Tong Pan, Jianhua Wang, Ni Yang*, Qun Sun*. Evaluation of volatile metabolites as potential markers to predict naturally-aged seed vigour by coupling rapid analytical profiling techniques with chemometrics. Food Chemistry，2021, DOI: 10.1016/j.foodchem.2021.130760

9. Keling Tu, Shaozhe Wen, Ying Cheng, Tingting Zhang, Tong Pan, Jie Wang, Jianhua Wang, Qun Sun*. A non-destructive and highly efficient model for detecting the genuineness of maize variety 'JINGKE 968' using machine vision combined with deep learning., 2021, DOI:10.1016/j.compag.202 Computers and Electronics in Agriculture1.106002

10. Ma J, Yang L*, Sun Q. Adaptive robust learning framework for twin support vector machine classification[J]. Knowledge-Based Systems, 2021, 211: 106536.

11. Quan Hu, Yanwei Zhang, Ruirui Ma, Jie An, Wenxuan Huang, Yueying Wu, Jingjing Hou, Dajian Zhang, Feng Lin, Ran Xu, Qun Sun*, Lianjun Sun*. Genetic dissection of seed appearance quality using recombinant inbred lines in soybean. Molecular Breeding, 2021, DOI: 10.1007/s11032-021-01262-9

12. Yanan Xu, Keling Tu, Ying Cheng, Haonan Hou, Hailu Cao, Xuehui Dong, Qun Sun*. Application of Digital Image Analysis to the Prediction of Chlorophyll Content in Astragalus Seeds. Applied Sciences, 2021, 11(18): 8744.

13. Ma J, Yang L*, Sun Q. Capped L1-norm Distance Metric-Based Fast Robust Twin Bounded Support Vector Machine[J]. Neurocomputing, 2020.(412): 295-311

14. Tingting Zhang, Shuxiang Fan, Yingying Xiang, Shujie Zhang, Jianhua Wang, Qun Sun*. Non-destructive Analysis of Germination Percentage, Germination Energy, and Simple Vigor Index on Wheat Seeds during Storage by Vis/NIR and SWIR Hyperspectral Imaging. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2020(239):118488

15. Tingting Zhang, Yingying Xiang, Liming Yang, Jianhua Wang, Qun Sun*. Wavelength Variable Selection Methods for Non-destructive Detection of the Viability of Single Wheat Kernel Based on Hyperspectral Imaging[J]. Spectroscopy and Spectral Analysis, 2019, 39(5): 1556-1562.

16. Tingting Zhang, Bin Zhao, Liming Yang, Jianhua Wang, Qun Sun*. Determination of Conductivity in Sweet Corn Seeds with Algorithm of GA and SPA Based on Hyperspectral Imaging Technique[J], Spectroscopy and Spectral Analysis, 2019, 39(8): 2608-2613.

17. Liming Yang∗, Boyan Yang, Shibo Jing, Qun Sun. A minimax probability extreme machine framework and its application in pattern recognition. Engineering Applications of Artificial Intelligence. 2019(81): 260–269.

18. Tingting Zhang, Wensong Wei, Bin Zhao, Ranran Wang, Mingliu Li, Liming Yang, Jianhua Wang, Qun Sun*. A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. Sensors, 2018, 18(3): 813.

19. TU Ke-ling, LI Lin-juan, YANG Li-ming, WANG Jian-hua, Qun Sun*. Selection for high quality pepper seeds by machine vision and classifiers，Journal of Integrative Agriculture  2018, 17(9): 60345-7

20. SUN Qun*，ZHU Liwei, ZHANG Wenjing, WANG Jianhua. Physical and Chemical Difference of Seed Coat Between Hard and Soft Seeds of Licorice (Glycyrrhiza Uralensis Fisch), Legume Research, 2018, 41(3): 441-446

21. 张婷婷，孙群*，杨磊，杨丽明，王建华. 基于电子鼻传感器阵列优化的甜玉米种子活力检测. 农业工程学报，2017, 33(21): 275-281

22. Yang Liming*, Sun Qun. Comparison of chemometric approaches for near-infrared spectroscopic data. Analytical Methods, 2016, 8(8): 1914-1923

23. Kaixia Wen, Zongming Xie, Liming Yang, Baoqi Sun, Jianhua Wang and Qun Sun*. Computer vision technology determines optimal physical parameters for sorting JinDan 73 maize seeds. Seed Science and Technology, 2015, 43: 62-70

24. Liming Yang*, Yongping Gao, Qun Sun. A new minimax probabilistic approach and its application in  recognition the  purity of hybrid seeds. Computer Modeling in Engineering & Sciences, 2015, 104: 493-506

25. 彭江南，谢宗铭，杨丽明，王建华，孙宝启，孙群*. 基于Seed Identification软件的棉籽机器视觉快速精选, 农业工程学报，2013，29(23): 147-152

26. Yang Liming*, Sun Qun. Recognition of the hardness of licorice seeds using a semi-supervised learning method and  near  infrared spectral data. Chemo metrics and Intelligent Laboratory Systems, 2012, 114: 109-115

27. Zhu Liwei, Huang Yanyan, Wang Qing, Ma Hanxu, Sun Baoqi，Sun Qun*. Nondestructive Identification of Hard seeds of Three Legume Plants Using Near Infrared Spectroscopy .Transaction of the Chinese Society of Agricultural Engineering.2012, 28(supp.2):237-242

28. Wang Qing, Xue Weiqing, Ma Hanxu, Li Junhui, Sun Baoqi, Sun Qun*. Quantitative Analysis of Seed Purity for Maize Using Near Infrared Spectroscopy. Transaction of the Chinese Society of Agricultural Engineering.2012,28(supp.2):259-264

29. 向莹莹，李浩卓，张婷婷，王建华，孙群*. 电导率法早期检测玉米和小麦种子活力. 中国农业大学学报，2020，25(06): 12-19.

30. 程莹，李浩卓，张婷婷，王建华，孙群*. 喷淋发芽箱的设计、制作及喷淋发芽检测方法的确定. 中国农业大学学报，2020，25(12): 01-08.

31. 张婷婷, 赵宾, 杨丽明, 王建华, 孙群*. 基于电子鼻技术的小麦种子活力鉴别, 中国农业大学学报，2018，23（9）：123-130

32. 叶凤林，李琳，杨丽明，王建华，孙群*. 应用机器视觉技术筛选射干种子精选指标的研究. 中国农业大学学报，2016,21(8):119-124

33. 叶凤林，李琳，杨丽明，王建华，孙群*. 基于机器视觉的黄芩种子精选技术研究. 种子，2016, 35(11): 100-104

34. 贾佳, 王建华, 谢宗铭, 杨丽明, 孙宝启, 孙群*.计算机图像识别技术在小麦种子精选中的应用，中国农业大学学报，2014，19(5): 180-186

35. 孙群*，王庆，薛卫青，马晗煦，孙宝启，谢宗铭. 无损检测技术在种子质量检验上的应用研究进展.中国农业大学学报，2012, 17(3): 1-6.

36. 黄艳艳，朱丽伟，李军会，王建华，孙宝启，孙群*. 应用近红外光谱技术快速鉴别玉米杂交种纯度的研究. 光谱学与光谱分析, 2011，31(3): 661-664

37. 黄艳艳，朱丽伟，马晗煦，李军会，孙宝启，孙群*. 应用近红外光谱技术定量分析杂交玉米纯度的研究 .光谱学与光谱分析, 2011，31(10)：2706-2710

38. 孙群，李欣，李航，吴坷，李军会，王建华，孙宝启*. 乌拉尔甘草种子硬实率的近红外光谱分析.光谱学与光谱分析, 2010, 30(1): 70-73.

39. 种子形态自动化识别软件，2013，软件著作权号：2013SRBJ0528

40. 小麦幼苗自动测量软件，2015，软件著作权：2015SRBJ0509

41. 基于计算机视觉的种子表型全自动化提取系统（Phenoseed）. 2019，软著登字第4855647号

42. 推拉式垂直板种子发芽装置，2015，发明专利ZL201410376643.6,

43. 种子碰撞试验装置及耐损伤能力检测方法，2017，发明专利ZL 201610089935.0