Jie Yang

About Me

I am currently a postdoctoral researcher at the University of Illinois Urbana-Champaign, working with Professors Kaiyu Guan and Bin Peng. My research focuses on developing advanced machine learning approaches to investigate sustainable agricultural production under climate change.
I received my Ph.D. in Biosystems Engineering from Zhejiang University, where I was advised by Professors K.C. Ting, Yibin Ying, and Tao Lin. Following that, I completed my first postdoctoral training at the University of Minnesota Twin Cities with Professor Zhenong Jin.

Research Interests

• Biogeochemical Processes in Agroecosystems: Water-carbon-nitrogen nexus, agricultural hydrology.
• Satellite-based Cropland Monitoring: Land use/cover mapping, crop yield prediction.
• Postharvest Technologies for Agroproducts: Food quality control, spectral/image processing.

News

• [2025/01] A collaborative study I contributed to has been published in Artificial Intelligence in Agriculture.
• [2024/12] I have delivered a talk on our graph machine learning approach for watershed nutrients prediction at American Geophysical Union Annual Meeting (AGU24).
• [2024/10] A collaborative study I contributed to has been published in Food Chemistry.
• [2024/09] I’m excited to start my new postdoctoral position at UIUC.
• [2024/09] I’m glad to share that I’ve been recognized as a winner in the Impactful Research category at the 2024 UMN Postdoc Awards.
• [2024/09] A collaborative study has been published in Postharvest Biology and Technology.
• [2024/07] Our study on tracing advanced chemometrics and deep learning toward robust spectral analysis for quality control has been published in Trends in Food Science & Technology.
• [2024/06] I have presented a poster on our sensor-model fusion framework for sustainable soil nutrient management at NSF Signals in the Soil (SitS) Workshop.
• [2024/06] Our study on mitigating model uncertainty in fruit quality control using knowledge-guided machine learning has been published in Postharvest Biology and Technology.
• [2024/04] I have delivered a poster presentation on our research simulating nitrogen dynamics at the crop field scale at ASABE North Central Regional Section Meeting.
• [2024/03] A collaborative study I contributed to has been published in AAAI-24.
• [2024/01] Our study on large-scale winter wheat mapping based on satellite imagery and harvester trajectory has been published in Computers and Electronics in Agriculture.

Publications

First/Corresponding Author Publications

(* for corresponding author, # for equal contribution)

• [7] Yang J, Sun Z, Tian S, Jiang H, Feng J, Ting KC, Lin T, & Ying Y (2024). Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty. Postharvest Biology and Technology, 216, 113009. Link
• [6] Zhang X & Yang J* (2024). Advanced chemometrics toward robust spectral analysis for fruit quality evaluation. Trends in Food Science & Technology, 150, 104612. Link
• [5] Xiong X#, Yang J#, Zhong R, Dong J, Huang J, Ting KC, Ying Y, & Lin T (2024). Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning. Computers and Electronics in Agriculture, 216, 108487. Link
• [4] Yang J, Luo X, Zhang X, Passos D, Xie L, Rao X, Xu H, Ting KC, Lin T, & Ying Y (2022). A deep learning approach to improving spectral analysis of fruit quality under interseason variation. Food Control, 140, 109108. Link
• [3] Yang J, Li J, Hu J, Yang W, Zhang X, Xu J, Zhang Y, Luo X, Ting KC, Lin T, & Ying Y (2022). An interpretable deep learning approach for calibration transfer among multiple near-infrared instruments. Computers and Electronics in Agriculture, 192, 106584. Link
• [2] Xu J#, Yang J#, Xiong X, Li H, Huang J, Ting KC, Ying Y, & Lin T (2021). Towards interpreting multi-temporal deep learning models in crop mapping. Remote Sensing of Environment, 264, 112599. Link
• [1] Yang J, Xu J, Zhang X, Wu C, Lin T, & Ying Y (2019). Deep learning for vibrational spectral analysis: Recent progress and a practical guide. Analytica Chimica Acta, 1081, 6-17. Link

Coauthor Publications

• [6] Sun Z, Tian H, Hu D, Yang J, Xie L, Xu H, & Ying Y (2025). Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy. Food Chemistry, 464, 141488. Link
• [5] Sun Z, Yang J, Hu D, Tian H, Ying Y, & Xie L (2024). Using knowledge-guided temperature correction for online non-destructive detection of soluble solids content in pear via Vis/NIR spectroscopy. Postharvest Biology and Technology, 218, 113178. Link
• [4] He E, Xie Y, Sun A, Zwart J, Yang J, Jin Z, Wang Y, Karimi H, & Jia X (2024). Fair Graph Learning Using Constraint-Aware Priority Adjustment and Graph Masking in River Networks. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-24), 38 (20), 22087-22095. Link
• [3] Zheng Y, Cao Y, Yang J, & Xie L (2023). Enhancing model robustness through different optimization methods and 1-D CNN to eliminate the variations in size and detection position for apple SSC determination. Postharvest Biology and Technology, 205, 112513. Link
• [2] Zhang X, Yang J, Lin T, & Ying Y (2021). Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends in Food Science & Technology, 112, 431-441. Link
• [1] Zhang X, Xu J, Yang J, Chen L, Zhou H, Liu X, Li H, Lin T, & Ying Y (2020). Understanding the learning mechanism of convolutional neural networks in spectral analysis. Analytica Chimica Acta, 1119, 41-51. Link

Academic Services

• Manuscript Reviewer for journals including Computers and Electronics in Agriculture, Hydrological Processes, Remote Sensing, and others.

Hobbies

• Swimming, badminton, basketball, hiking, photography, and cooking.