个人简介

田世杰，男，中共党员，西北农林科技大学信息工程学院副教授，农业农村部农业物联网重点实验室成员。2022年10月至2023年11月前往新西兰奥克兰大学进行联合培养，2024年3月博士毕业于浙江大学。主要从事图谱深度学习、智能感知与决策、高通量传感装备等智慧农业相关理论与应用研究，截止2024年3月发表SCI/EI收录论文30余篇，其中一作论文11篇（2篇为校级G2高水平期刊论文）

教育经历

2020年9月-2024年3月，浙江大学，工学博士

2022年10月-2023年11月，UOA(University of Auckland)，CSC资助联合培养

2017年9月-2020年6月，西北农林科技大学，工学硕士

2012年9月-2016年6月，西北农林科技大学，工学学士

工作经历

2024年4月-至今，西北农林科技大学信息工程学院，副教授

2016年8月-2017年7月，西北农林科技大学研究生支教团，陕西丹凤县支教教师

学术兼职

获奖和荣誉

2023年，浙江大学智能生物产业装备创新（IBE）团队学术故事奖

2022年，浙江大学优秀研究生、康而达奖学金、学院学术成果奖

2022年，浙江大学博士研究生国家奖学金

2022年，国家留学基金委（CSC）公派研究生项目奖学金

2022年，浙江大学智能生物产业装备创新（IBE）团队学术故事奖

2021年，浙江大学优秀研究生、三好研究生、优秀学生党务工作者

2021年，浙江大学智能生物产业装备创新（IBE）团队学术故事奖、优秀博士生奖

2019年，西北农林科技大学硕士研究生国家奖学金

2019年，农业工程学会学生优秀报告

2018年，中国研究生电子设计竞赛全国总决赛团队一等奖

2018年，西北农林科技大学优秀研究生

2017年，西北农林科技大学优秀研究生

生物表型技术（包括但不局限于以下方向）

1. 生物高通量三维生理-表型信息的时空同步感知方法及关键装备

该研究涵盖了通过图谱技术同步捕捉并分析生物体（例如植物）在不同时间和空间尺度上的生理与表型信息。这一技术使我们能够实时观察到生物体的生长状况与病理变化，并以三维形式全面洞察生物体的健康状态。此外，图谱融合技术能够整合生物体的三维成像数据与其他生理数据（如叶绿素含量、营养元素等），进而深入解析生物体的多重生理状态。

2. 作物生理-表型-环境耦合的多参数生长模型构建

本研究致力于开发一种综合模型，该模型集成了作物的生理特征、表型表达及环境因素。借助此模型，我们能预测作物在不同环境条件下的生长表现，从而有助于作物品种的选育和高效种植。

3. 作物-土壤-环境协同调控技术与智能管理装备开发

该研究专注于运用智能控制、决策技术及智能设备，优化作物、土壤与环境的相互作用。通过智能化管理，实现对农业生产中各种因素（如灌溉、施肥等）的实时监控与调整，适应不断变化的环境条件，以提高作物的潜在最大产量。

果蔬产后商品化处理技术（包括但不局限于以下方向）

1. 光、声信息与果蔬产品互作机理、果蔬内部品质无损感知方法

该研究方向旨在利用光学和声振技术探测果蔬的内部品质。通过光谱分析，我们可以捕捉到果蔬内部理化相关的信息，如内部病害、糖度等，而声振技术可以探测到果蔬内部结构的变化，如硬度。结合这两种技术，可以实现对果蔬内部品质的全面无损检测。

2. 集成机械手和传感器的果蔬品质实时监测技术

本研究关注如何将精准的机械手臂与先进的传感器技术相结合，实现对果蔬品质的实时监控和自动处理。通过在机械手臂上安装传感器，可以在果蔬处理过程中实时监测其外部和内部品质。这项技术对提升现代农业生产线的自动化和智能化水平具有重要意义，有助于优化生产效率。

3. 果蔬高通量分选技术与智能决策系统研究

高通量分选技术利用自动化设备，结合机器视觉、光谱、机械自动化和智能算法，快速有效的对大量果蔬进行分拣和分类。该技术可以依据果蔬的大小、颜色、内部品质、内部缺陷等多个参数进行快速筛选。此外，智能决策系统可以根据收集到的实时数据，做出最优的处理和分拣决策，极大提升分拣效率和准确性。

细化方向、技术、算法可来办公室讨论

课题组隶属于农业物联网重点实验室，特别欢迎各位同学加入！

研究领域具有多学科高度交叉的特点，欢迎有计算机科学与技术、农业工程、光学工程、电子信息、机械工程、植物科学等专业背景的学生联系我攻读硕士、博士学位，

热烈欢迎本科生来课题组交流并进行科研项目训练（可发邮件，也可来办公室面谈）。

课题组不强制要求科研时间、地点，但会定期召开组会交流科研进展，我也会一对一与学生交流近期学习和生活状态。

课题组可为表现良好者提供推荐，前往国内外著名高校（浙江大学、奥克兰大学、京都大学、华盛顿州立大学等）进行深造或联合培养。

求学期间科研与生活压力不容小觑，不希望课题组学生受到伤害，所以希望加入课题组的同学能够乐观开朗、善于沟通合作，能够愿意学习编程与算法。

浙江大学–浙江开浦科技联合研究中心揭榜挂帅项目，桃子硬度在线无损检测分类方法建立（2022ZKP001），主持；

国家重点研发计划，特色果蔬品质无损检测及智能分选装备创制与应用（2022YFD2002200），主要参与；

2022年度浙江“领雁”研发攻关计划项目，西瓜高效机械化采收与产地处理装备，主要参与；

国家自然科学基金委员会联合基金项目，农产品品质性状信息获取机理与关键科学问题研究（U20A2019），主要参与；

广东省重点领域研发计划项目，广东蜜柚品质在线检测和分级成套智能装备研发（2018B020240001），主要参与；

2023年，中国农业机械学会60周年会庆暨2023年学术年会（云南昆明），报告

2022年，新西兰食品科学年会（新西兰但尼丁），报告

2019年，中国农业工程年会（浙江杭州），报告

[2] Z Wei, H Liu, J Xu, Y Li, J Hu*, S Tian*, 2024. Quality grading method for Pleurotus eryngii during postharvest storage based on hyperspectral imaging and multiple quality indicators. Food Control, 110763.  (JCR中科院一区)

[1] M Lu, H Wang, J Xu, Z Wei, Y Li, J Hu*, S Tian, 2024. A Vis/NIRS device for evaluating leaf nitrogen content using K-means algorithm and feature extraction methods. Computers and Electronics in Agriculture, 225, 109301.  (JCR中科院一区)

2024年3月之前（合作发表不列出）：

1. Guo, M.#, Tian, S.#(共一), Wang, W., Xie, L., Xu, H.*, Huang, K.*, 2024. Biomimetic leaves with immobilized catalase for machine learning-enabled validating fresh produce sanitation processes. Food Research International, 179, 114028. https://doi.org/10.1016/j.foodres.2024.114028 (JCR中科院一区，校G2）

2. Tian, S., Liu, W., Xu, H.*, 2023. Improving the prediction performance of soluble solids content (SSC) in kiwifruit by means of near-infrared spectroscopy using slope/bias correction and calibration updating. Food Research International, 170, 112988. https://doi.org/10.1016/j.foodres.2023.112988 (JCR中科院一区，校G2）

3. Tian, S., Wang, S., Xu, H.*, 2022. Early detection of freezing damage in oranges by online Vis/NIR transmission coupled with diameter correction method and deep 1D-CNN. Computers and Electronics in Agriculture 106638. https://doi.org/10.1016/j.compag.2021.106638 (JCR中科院一区）

4. Tian, S., Wang, J., Xu, H.*, 2022. Firmness measurement of kiwifruit using a self-designed device based on acoustic vibration technology. Postharvest Biology and Technology, 187, 111851. https://doi.org/10.1016/j.postharvbio.2022.111851 (JCR中科院一区）

5. Tian, S., Tian, H., Yang, Q., Xu, H.*, 2022. Internal quality assessment of kiwifruit by bulk optical properties and online transmission spectra. Food Control, 141, 109191. https://doi.org/10.1016/j.foodcont.2022.109191 (JCR中科院一区）

6. Tian, S., Qu, M., Xu, H.*, 2023. Establishment of evaluation criterion based on starch dyeing method and implementation of optical and acoustic techniques for postharvest determination of “HongYang” kiwifruit ripeness. European Journal of Agronomy, 142, 126682. https://doi.org/10.1016/j.eja.2022.126682 (JCR中科院一区）

7. Tian, S., Xu, H.*, 2022. Nondestructive methods for the quality assessment of fruits and vegetables considering their physical and biological variability. Food Engineering Reviews. https://doi.org/10.1007/s12393-021-09300-0 (JCR一区，中科院二区）

8. Tian, S., Xu, H.*, 2022. Mechanical-based and optical-based methods for nondestructive evaluation of fruit firmness. Food Reviews International. https://doi.org/10.1080/87559129.2021.2015376 (JCR一区，中科院二区）

9. Tian, S., Zhang, J., Zhang, Z., Zhao, J., Zhang, Z., Zhang, H.*, 2019. Effective modification through transmission Vis/NIR spectra affected by fruit size to improve the prediction of moldy apple core. Infrared Physics & Technology 100, 117-124. https://doi.org/10.1016/j.infrared.2019.05.015 (JCR中科院二区）

10. Tian, S., Zhang, M., Li, B., Zhang, Z., Zhao, J.*, Zhang, Z., Zhang, H., Hu, J.*, 2020. Measurement orientation compensation and comparison of transmission spectroscopy for online detection of moldy apple core. Infrared Physics & Technology 111. https://doi.org/10.1016/j.infrared.2020.103510 (JCR中科院二区）

11. 张海辉*, 田世杰, 马敏娟, 赵娟, 张军华, 张佐经. 考虑直径影响的苹果霉心病透射光谱修正及检测. 农业机械学报, 2019, 50(1):313-320. http://www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20190135&flag=1 (EI权威期刊）

12. 徐惠荣*, 田世杰, 应义斌, 吴俊哲, 李麟. 一种用于套袋水果自动去袋的装置, 2022, 发明专利, CN202110661369.7.

Personal profile

Shijie Tian, male, member of the Communist Party of China, Associate Professor at the College of Information Engineering, Northwest A&F University, and a member of the Key Laboratory of Agricultural Internet of Things under the Ministry of Agriculture and Rural Affairs. From October 2022 to November 2023, he was jointly trained at the University of Auckland, New Zealand, and earned his Ph.D. from Zhejiang University in March 2024. His research focuses on graph deep learning, intelligent sensing and decision-making, and high-throughput sensing equipment relevant to smart agriculture. As of March 2024, he has published over 30 SCI/EI-indexed papers, including 11 as the first author, two of which are high-level G2 journal papers.

Educational Background

Ph.D. in Engineering, Zhejiang University, September 2020-March 2024

Joint training at UOA (University of Auckland) funded by CSC, October 2022-November 2023

Master of Engineering, Northwest A&F University, September 2017-June 2020

Bachelor of Engineering, Northwest A&F University, September 2012-June 2016

Professional Experience

Associate Professor, College of Information Engineering, Northwest A&F University, April 2024-Present

Teaching staff, Shaanxi Danfeng County, August 2016-July 2017

Academic Positions

Awards and Honors

2023: Academic Story Award, IBE Team, Zhejiang University

2022: Outstanding Graduate Student, Kang Erda Scholarship, Academic Achievement Award, Zhejiang University

2022: National Scholarship for Ph.D. students, Zhejiang University

2022: CSC Scholarship for Ph.D. students studying abroad

2022: Academic Story Award, IBE Team, Zhejiang University

2021: Outstanding Graduate Student, Excellent Student Party Worker, Zhejiang University

2021: Academic Story Award, Outstanding doctoral student, IBE Team, Zhejiang University

2019: National Scholarship for Master's students, Northwest A&F University

2019: Excellent Student Report, Agricultural Engineering Society

2018: First Prize, National Final of the China Graduate Electronic Design Competition

2018: Outstanding Graduate Student, Northwest A&F University

2017: Outstanding Graduate Student, Northwest A&F University

Biological Phenotyping Technologies (including but not limited to)

1. High-throughput three-dimensional physiological-phenotypic information sensing methods and key equipment: This research includes synchronous capture and analysis of physiological and phenotypic information of organisms (e.g., plants) across different times and spatial scales using graph technology. This allows real-time observation of the growth and pathological changes in organisms, providing a comprehensive three-dimensional insight into their health.

2. Multi-parameter growth model construction coupled with crop physiology, phenotype, and environment: This study aims to develop an integrated model that incorporates the physiological traits, phenotypic expression, and environmental factors of crops. This model helps predict crop growth under various environmental conditions, aiding in crop selection and efficient cultivation.

3. Crop-soil-environment collaborative regulation techniques and smart management equipment development: The research focuses on using intelligent control, decision-making technologies, and smart devices to optimize the interactions between crops, soil, and environment. This intelligent management allows for real-time monitoring and adjustment of various agricultural production factors, such as irrigation and fertilization, adapting to changing environmental conditions to maximize potential crop yield.

Post-harvest Commercialization Technologies for Fruits and Vegetables (including but not limited to)

1. Interaction mechanisms between light/sound information and fruit and vegetable products, non-destructive internal quality sensing methods: This research direction aims to use optical and acoustic technologies to detect the internal quality of fruits and vegetables. Spectral analysis captures physicochemical information such as internal diseases and sugar content, while acoustic technology detects changes in the internal structure, such as hardness.

2. Real-time monitoring technology for fruit and vegetable quality integrating robotic arms and sensors: This study focuses on how to integrate precise robotic arms with advanced sensor technology to monitor and automatically process the quality of fruits and vegetables in real-time during handling.

3. High-throughput sorting technology and intelligent decision-making systems for fruits and vegetables: High-throughput sorting technology uses automated equipment combined with machine vision, spectroscopy, mechanical automation, and smart algorithms to quickly and effectively sort and classify large quantities of fruits and vegetables based on multiple parameters such as size, color, internal quality, and defects.

More technical and algorithmic details can be discussed in the office.

Our group, part of the Key Laboratory of Agricultural Internet of Things, warmly welcomes students to join us!

We are open to students interested in pursuing master's or doctoral degrees and welcome undergraduates to engage in research projects within our group.

Our group offers flexibility in research time and location, holds regular meetings to discuss research progress, and I personally discuss academic and life conditions with students one-on-one.

Well-performing members may receive recommendations for further studies or joint training at prestigious universities at home and abroad, such as Zhejiang University, the University of Auckland, Kyoto University, and Washington State University.

Zhejiang University – Zhejiang KaiPu Technology Joint Research Center Project, Online Non-Destructive Detection and Classification Methods for Peach Firmness (2022ZKP001), Principal Investigator.

National Key R&D Program, Non-Destructive Detection of Specialty Fruits and Vegetables Quality and Intelligent Sorting Equipment Creation and Application (2022YFD2002200), Key Participant.

2022 Zhejiang Leading Talent R&D Program, Efficient Mechanized Harvesting and Local Treatment Equipment for Watermelons, Key Participant.

Joint Fund Project of the National Natural Science Foundation of China, Research on Quality Trait Information Acquisition Mechanisms and Key Scientific Issues of Agricultural Products (U20A2019), Key Participant.

Guangdong Province Key R&D Program, Development of Complete Intelligent Equipment for Online Detection and Grading of Guangdong Honey Pomelo Quality (2018B020240001), Key Participant.

2023: Presentation at the 60th Anniversary Celebration and 2023 Academic Annual Meeting of the Chinese Society of Agricultural Machinery (Kunming, Yunnan, China).

2022: Presentation at the New Zealand Food Science Annual Conference (Dunedin, New Zealand).

2019: Presentation at the Chinese Agricultural Engineering Annual Conference (Hangzhou, Zhejiang, China).

[2] Z Wei, H Liu, J Xu, Y Li, J Hu*, S Tian*, 2024. Quality grading method for Pleurotus eryngii during postharvest storage based on hyperspectral imaging and multiple quality indicators. Food Control, 110763.  (JCR中科院一区)

[1] M Lu, H Wang, J Xu, Z Wei, Y Li, J Hu*, S Tian, 2024. A Vis/NIRS device for evaluating leaf nitrogen content using K-means algorithm and feature extraction methods. Computers and Electronics in Agriculture, 225, 109301.  (JCR中科院一区)

Before March 2024 (not listed for collaborative publication) :

1. Guo, M.#, Tian, S.#, Wang, W., Xie, L., Xu, H.*, Huang, K.*, 2024. Biomimetic leaves with immobilized catalase for machine learning-enabled validating fresh produce sanitation processes. Food Research International, 179, 114028. https://doi.org/10.1016/j.foodres.2024.114028

2. Tian, S., Liu, W., Xu, H.*, 2023. Improving the prediction performance of soluble solids content (SSC) in kiwifruit by means of near-infrared spectroscopy using slope/bias correction and calibration updating. Food Research International, 170, 112988. https://doi.org/10.1016/j.foodres.2023.112988

3. Tian, S., Wang, S., Xu, H.*, 2022. Early detection of freezing damage in oranges by online Vis/NIR transmission coupled with diameter correction method and deep 1D-CNN. Computers and Electronics in Agriculture 106638. https://doi.org/10.1016/j.compag.2021.106638

4. Tian, S., Wang, J., Xu, H.*, 2022. Firmness measurement of kiwifruit using a self-designed device based on acoustic vibration technology. Postharvest Biology and Technology, 187, 111851. https://doi.org/10.1016/j.postharvbio.2022.111851

5. Tian, S., Tian, H., Yang, Q., Xu, H.*, 2022. Internal quality assessment of kiwifruit by bulk optical properties and online transmission spectra. Food Control, 141, 109191. https://doi.org/10.1016/j.foodcont.2022.109191

6. Tian, S., Qu, M., Xu, H.*, 2023. Establishment of evaluation criterion based on starch dyeing method and implementation of optical and acoustic techniques for postharvest determination of “HongYang” kiwifruit ripeness. European Journal of Agronomy, 142, 126682. https://doi.org/10.1016/j.eja.2022.126682

7. Tian, S., Xu, H.*, 2022. Nondestructive methods for the quality assessment of fruits and vegetables considering their physical and biological variability. Food Engineering Reviews. https://doi.org/10.1007/s12393-021-09300-0

8. Tian, S., Xu, H.*, 2022. Mechanical-based and optical-based methods for nondestructive evaluation of fruit firmness. Food Reviews International. https://doi.org/10.1080/87559129.2021.2015376

9. Tian, S., Zhang, J., Zhang, Z., Zhao, J., Zhang, Z., Zhang, H.*, 2019. Effective modification through transmission Vis/NIR spectra affected by fruit size to improve the prediction of moldy apple core. Infrared Physics & Technology 100, 117-124. https://doi.org/10.1016/j.infrared.2019.05.015

10. Tian, S., Zhang, M., Li, B., Zhang, Z., Zhao, J.*, Zhang, Z., Zhang, H., Hu, J.*, 2020. Measurement orientation compensation and comparison of transmission spectroscopy for online detection of moldy apple core. Infrared Physics & Technology 111. https://doi.org/10.1016/j.infrared.2020.103510

11. 张海辉*, 田世杰, 马敏娟, 赵娟, 张军华, 张佐经. 考虑直径影响的苹果霉心病透射光谱修正及检测. 农业机械学报, 2019, 50(1):313-320. http://www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20190135&flag=1

12. Xu Huirong*, Tian Shijie, Ying Yibin, Wu Junzhe, Li Lin. A Device for Automatically Removing Bags from Bagged Fruit, 2022, Invention Patent, CN202110661369.7.