姓名：田喜

职称：副研究员

所属系所：智慧农业系

办公地点：北京科技大学西三旗校区8号楼（北京市海淀区建材城东路10号京城尚德智造产业园）

办公电话：010-82917178

电子邮件：tianx@ustb.edu.cn

科研方向：农作物表型机器人，农业智能装备

社会职务：

1. 《Plant Phenomics》、《Journal of Future Foods》、《农业工程》青年编辑委员会委员

2. 中国仪器仪表学会标准化工作红外光谱技术委员会，总干事

简 历：

2024.09-至今 北京科技大学 生物农业研究院 副研究员

2022.07-2024.08 北京市农林科学院智能装备技术研究中心 高级农艺师 博士后

2018.09-2022.06 中国农业大学 农业电气化与自动化 工学博士 导师：赵春江 院士

2014.07-2018.08 北京市农林科学院智能装备技术研究中心 工程师

2011.09-2014.06 西南大学 果树学 农学硕士 导师：邓烈 研究员

2007.09-2011.06 山东滨州学院 生态学 理学学士

科研业绩: 

近年来主持国家自然科学基金青年基金等项目共5项，以第一/通讯作者在Food Chemistry、Computers and Electronics in Agriculture、Postharvest Biology and Technology等期刊发表SCI论文24篇，荣获“中国仪器仪表学会第五届陆婉珍近红外光谱青年奖”，入选“2020年北京市科协青年人才托举工程”和“全球前2%顶尖科学家2024年度影响力榜单”，参编学术专著1部，获得授权国家发明专利5项。

招生计划：每年招收1-2名硕士研究生

研究生培养情况：独立培养硕士研究生4人，联合培养博士研究生3人，硕士研究生6人。

获得奖励/发明专利：

1. 2025年度北京科技大学第二届研究生教育教学成果奖-特等奖

2. 2025年度中国国际大学生创新大赛北京赛区一等奖

3. 2023~2024/2024~2025年度连续入选全球前2%顶尖科学家

4. 2022年度第五届中国仪器仪表学会陆婉珍近红外光谱奖青年奖

5. 2019年度河南省教育厅科技成果奖二等奖

6. 2020年度入选“北京市科学技术协会青年人才托举工程”

2、获得国家发明专利

（1）田喜，庞琦，黄文倩，张驰. 一种水果表皮缺陷检测方法及系统，专利号：202110044367.3

（2）田喜，黄文倩，李江波，竹永伟. 用于检测柑橘表皮缺陷的在线图像采集系统及方法，专利号：201610755270.2

（3）田喜，李江波，黄文倩，刘宸，樊书祥. 一种柑橘早期腐烂的透射成像检测方法，专利号：201911192624.7

（4）田喜，黄文倩，樊书祥，李江波，夏宇. 一种基于单积分球的农产品组织光学特性参数检测装置，专利号：201810166553.2

（5）田喜，黄文倩，李江波. 一种水果内部品质无损检测模型建立方法及系统，专利号：201810620936.2

代表性论文论著：

1. 田喜, 朱烨, 张书瑜, 宋扬, 贺勇, 李雪男, 赵春江* &万向元*.(2026). 高通量玉米表型检测技术及装备研究进展. 科学通报, 71(20), 4849-4864.

2. Zhang, T., Wen, J., Tang, Y., Tong, J., Li, M., Zhang, Z., Tian, X.*, & Gu, L.* (2026). Empowering Chinese medicinal agriculture through AI-driven technologies: A comprehensive review. Artificial Intelligence in Agriculture, 16, 889-910.

3. Yao, X., Li, X., Zhang, S., Song, Y., He, Y., Tian, X.*, & Wan, X.* (2026). Accurate detection of full-surface ear rot in maize using hyperspectral imaging and deep learning. Food Research International, 119659.

4. Fan, Y., An, T., Tian, X., Huang, W., & Wang, Q.* (2026). Riemannian geometry–based structured modeling of excitation–emission matrix fluorescence for nondestructive single-kernel maize seed viability assessment. Computers and Electronics in Agriculture, 252, 112121.

5. Fan, Y., Yao, X., Wang, Z., Long, Y., Kang, J., Chen, L., Huang, W., & Tian, X.* (2025). Combining dual-wavelength laser-induced fluorescence hyperspectral imaging with mutual information decomposition and redundancy elimination method to detect Aflatoxin B1 of individual maize kernels. Computers and Electronics in Agriculture, 239, 110938.

6. Fan, Y., An, T., Yao, X., Long, Y., Wang, Q., Wang, Z., Tian, X., Chen, L., & Huang, W.* (2025). An interpretable nondestructive detection model for maize seed viability: Based on grouped hyperspectral image fusion and key biochemical indicators. Computers and Electronics in Agriculture, 239, 111036.

7. Tian, X., Yao, J., Yu, H., Wang, W., & Huang, W.* (2024). Early contamination warning of Aflatoxin B1 in stored maize based on the dynamic change of catalase activity and data fusion of hyperspectral images. Computers and Electronics in Agriculture, 217, 108615.

8. An, T., Fan, Y., Tian, X., Wang, Q., Wang, Z., Fan, S., & Huang, W. (2024). Green analytical assay for the viability assessment of single maize seeds using double-threshold strategy for catalase activity and malondialdehyde content. Food Chemistry, 455, 139889.

9. Tian, X., Zhang, C., Li, J., Fan, S., Yang, Y., & Huang, W. (2021). Detection of early decay on citrus using LW-NIR hyperspectral reflectance imaging coupled with two-band ratio and improved watershed segmentation algorithm. Food Chemistry, 360, 130077.

10. Tian, X., Li, J., Wang, Q., Fan, S., & Huang, W.* (2018). A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and peel pigments. Food Chemistry, 239, 1055-1063.

11. Tian, X., Fan, S., Li, J., Huang, W., & Chen, L.* (2020). An optimal zone combination model for on-line nondestructive prediction of soluble solids content of apple based on full-transmittance spectroscopy. Biosystems Engineering, 197, 64-75.

12. Tian, X., Li, J., Wang, Q., Fan, S., Huang, W., & Zhao, C. (2019). A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging. Biosystems Engineering, 183, 110-120.

13. Tian, X., Wang, Q., Huang, W., Fan, S., & Li, J.* (2020). Online detection of apples with moldy core using the Vis/NIR full-transmittance spectra. Postharvest Biology and Technology, 168, 111269.

14. Tian, X., Fan, S., Huang, W., Wang, Z., & Li, J.* (2020). Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms. Postharvest Biology and Technology, 161, 111071.

15. Yao, X., Fan, Y., Wang, Q., Huang, W., Zhao, C., & Tian, X.* (2025). Data fusion strategy for nondestructive detection of Aflatoxin B1 content in single maize kernel using dual-wavelength laser-induced fluorescence hyperspectral imaging. Food and Bioprocess Technology, 1-17.

16. Han, X., Li, X., Wu, S., Liu, X., Li, Z., Song, Y., Tian, X., & Wan, X. (2026). Hyperspectral imaging combined with texture features for maize hybrid purity detection: a multi-model comparison based on machine learning and SHAP interpretability study. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 128407.

17. Wang, Z., An, T., Wang, W., Fan, S., Chen, L., & Tian, X.* (2023). Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 122679.

18. Tian, X., Li, J., Yi, S., Jin, G., & Li, Y.* (2020). Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm. Artificial Intelligence in Agriculture, 4, 48-57.

19. Fan, Y., An, T., Wang, Q., Yang, G., Huang, W., Wang, Z., & Tian, X.* (2023). Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network. Frontiers in Plant Science, 14, 1248598.

20. Tian, X., Liu, X., He, X., Zhang, C., Li, J., & Huang, W.* (2023). Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm. Journal of the Science of Food and Agriculture, 103, 6689-6705.