Name：Xin Zhou

Position and Technical Title：Department Head, Associate Professor, Master’s Supervisor

Research Areas：Nondestructive Detection, Spectroscopy, Image Information Processing, Deep Learning

Graduate Admission Professions：Agricultural Engineering

Telephone：0511-80829151

E-mail：zhouxin_21@ujs.edu.cn

Corresponding Address：School of Low-Altitude Technology, Jiangsu University, No.301, Xuefu Road, Zhenjiang, Jiangsu Province(212013)

Education and Work Experience

Jun. 2017–Dec. 2020  Doctor of Agricultural Engineering, School of Electrical and Information Engineering, Jiangsu University

Sep. 2014–Jun. 2017  Master of Signal and Information Processing, School of Electrical and Information Engineering, Jiangsu University

Sep. 2010–Jun. 2014  Bachelor of Electronic Information Engineering, School of Electrical and Information Engineering, Jiangsu University

Academic and Social Part-time

Serve as a reviewer for international journals including Bioresource Technology, Computers and Electronics in Agriculture and Journal of the Science of Food and Agriculture.

Teaching Courses              

Electrical and Electronic Engineering A (Ι), Electrical and Electronic Engineering A (Ⅱ)

Academic Research Topic

[1] National Natural Science Foundation of China (NSFC) Youth Program, Grant No. 32201653, Jan. 2023 – Dec. 2025, Principal Investigator, Ongoing

[2] General Program of China Postdoctoral Science Foundation, Grant No. 2021M701479, Nov. 2021 – Dec. 2023, Principal Investigator, Completed

[3] General Program of National Natural Science Foundation of China (NSFC), Grant No. 31971788, Jan. 2020 – Dec. 2023, Participant, Completed

Academic Publications

[1] Zhou Xin*, Zhao Chunjiang, Sun Jun*, Cao Yan, Yao Kunshan, Xu Min. A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging. Food Chemistry, 2023, 409: 135251.

[2] Zhou Xin*, Zhao Chunjiang, Sun Jun, Yao Kunshan, Xu Min, Cheng Jiehong. Nondestructive testing and visualization of compound heavy metals in lettuce leaves using fluorescence hyperspectral imaging. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2023, 291: 122337.

[3] Zhou Xin*, Zhao Chunjiang, Sun Jun*, Yao Kunshan, Xu Min. Detection of lead content in oilseed rape leaves and roots based on deep transfer learning and hyperspectral imaging technology. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2023, 290: 122288.

[4] Zhou Xin*, Sun Jun*, Tian Yan, Yao Kunshan, Xu Min. Detection of heavy metal lead in lettuce leaves based on fluorescence hyperspectral technology combined with deep learning algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 266: 120460.

[5] Zhou Xin*, Zhao Chunjiang, Sun Jun*, Cao Yan, Fu lvhui. Classification of heavy metal Cd stress in lettuce leaves based on WPCA algorithm and fluorescence hyperspectral technology. Infrared Physics & Technology, 2021, 119: 103936.

[6] Zhou xin, Sun Jun*, Tian Yan, Lu Bing, Hang Yingying, Chen Quansheng. Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce. Food Chemistry, 2020,321:126503.

[7] Zhou xin, Sun Jun*, Tian Yan, Chen Quansheng, Wu Xiaohong, Hang Yingying. A Deep Learning Based Regression Method on Hyperspectral Data for Rapid Prediction of Cadmium Residue in Lettuce Leaves. Chemometrics and Intelligent Laboratory Systems, 2020,200:103996.

[8] Zhou Xin, Sun Jun*, Wu Xiaohong, Lu Bing, Yang Ning, Dai Chunxia. Research on moldy tea feature classification based on WKNN algorithm and NIR hyperspectral imaging. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 206: 378-383

[9] Zhou Xin, Sun Jun*, Tian Yan, Lu Bing, Hang Yingying, Chen Quansheng. Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images. International Journal of Remote Sensing, 2019,41(6):2263-2276.

[10] Sun Jun, Zhou Xin*, Hu Yongguang, Wu Xiaohong, Zhang Xiaodong, Wang Pei. Visualizing Distribution of Moisture Content in Tea Leaves Using Optimization Algorithms and NIR Hyperspectral Imaging[J]. Computers and Electronics in Agriculture. 2019, 160: 153-159.