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 systems.
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

• Agroecosystem Modeling: Knowledge-guided AI for understanding soil-water-nutrient interactions and their impacts on hydrology and water quality across agricultural landscapes.
• Digital Agriculture: Multi-scale sensing (from satellite to in situ) and modeling for cropland monitoring, crop yield prediction, and food and agro-product quality assessment.

News

• [2026/06] Our study revealing hydrologic controls of riverine nitrous oxide (N₂O) emission hotspots and hot moments across the contiguous US has been published in PNAS.
• [2026/03] Our study on knowledge-guided graph machine learning for daily, spatially distributed hydrology and nitrogen export modeling has been published in Water Research.
• [2026/02] Our study on knowledge-guided graph machine learning for large-scale crop yield mapping has been published in Remote Sensing of Environment.
• [2026/01] Our study on hyperspectral imaging-based early plant disease detection has been published in Future Foods.
• [2026/01] Our study on design and field testing of a lower-cost nitrate sensor has been published in ACS ES&T Water.
• [2026/01] Our review on structure-engineered colorimetric sensors for volatile amine detection has been published in Trends in Analytical Chemistry.
• [2025/12] Our study introducing an interpretable multimodal deep learning framework for fruit quality control has been published in Artificial Intelligence in Agriculture.
• [2025/12] I presented two oral talks at AGU25 on our advances in knowledge-guided machine learning, with applications to crop yield mapping and nutrient transport modeling.
• [2025/08] Our study on explicable attention mechanisms to improve fruit quality control has been published in Computers and Electronics in Agriculture.
• [2025/06] Our perspective article exploring the application of LLMs in hydrology has been published in Environmental Research: Water.
• [2025/05] I presented a talk on spatially distributed watershed modeling in the Upper Mississippi River Basin at Society for Freshwater Science 2025 Annual Meeting.
• [2025/04] Our study introducing spatial reasoning-enhanced multimodal LLMs for CAD model synthesis has been accepted by AAAI-25.
• [2025/01] Our study proposing a knowledge-guided learning approach has been published in Artificial Intelligence in Agriculture.
• [2024/12] I delivered a talk on our graph machine learning approach for watershed nutrients prediction at American Geophysical Union Annual Meeting (AGU24).
• [2024/10] Our study on multi-source data fusion has been published in Food Chemistry.
• [2024/09] I’m excited to start my new postdoctoral position at University of Illinois Urbana-Champaign (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] Our study introducing a knowledge-guided method to improve spectroscopy-based fruit quality control has been published in Postharvest Biology and Technology.
• [2024/07] Our review on advanced chemometrics for robust spectral analysis in fruit quality control has been published in Trends in Food Science & Technology.
• [2024/06] I presented a poster on a 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 presented a poster on simulating nitrogen dynamics at the crop field scale at ASABE North Central Regional Section Meeting.
• [2024/03] Our study on fairness-aware GNNs has been accepted by 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 (* Corresponding author, # Equal contribution)

• [24] Yang J, Peng B, Wang Y, Ma Z, Zhao Q, Liu L, Jia X, Kumar V, Pan M, Nieber J, Jin Z, & Guan K (2026). Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics. Water Research, 297, 125613. Link
• [23] Yang J, Liu L, Yang Q, Jia X, Peng B, Guan K, & Jin Z (2026). Knowledge-guided graph machine learning improves corn yield mapping in the U.S. Midwest. Remote Sensing of Environment, 335, 115287. Link
• [22] Zhang X & Yang J* (2024). Advanced chemometrics toward robust spectral analysis for fruit quality evaluation. Trends in Food Science & Technology, 150, 104612. (ESI Highly Cited Paper) Link
• [21] 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
• [20] 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
• [19] 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
• [18] 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
• [17] 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. (ESI Highly Cited Paper) Link
• [16] 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

• [15] Hu M, Yu Z, Griffis T J, Aho K, Wang Y, Yang J, Yang W H, Bernacchi C J, McGrath J M, Dahlgren R, Tian H, & Baker J M (2026). Hydrologic connectivity amplifies riverine N₂O emission hotspots and hot moments across the continental United States. Proceedings of the National Academy of Sciences, 123(26), e2524113123. Link
• [14] Yang M, Xu X, Wang X, Yang J, Zhang X, Pan Q, Wang C, & Zhang X (2026). Enhancing early detection of mango anthracnose using hyperspectral imaging and 3D-CNNs. Future Foods, 13, 100929. Link
• [13] Furuta D, Yang J, Liu L, Jin Z, Guan K, Peng B, & Li J (2026). Design and test of a lower-cost water-quality sensor for nitrate. ACS ES&T Water, 6(2), 709−719. Link
• [12] Peng B, Li Y, Qin J, Qiao Z, Li Y, Yang J, Zhou P, Zhao X, Kuang Y, Wu K, & Jiang F (2026). Structure-engineered colorimetric sensors for volatile amine detection: Advances in freshness monitoring of meat and aquatic products. Trends in Analytical Chemistry, 196, 118661. Link
• [11] Xu X, Yang M, Yang J, Yin H, Chen Y, Lan W, Sun G, Wang X, Zhang S, & Zhang X (2026). SpecColorNet: An interpretable multimodal deep learning approach for predicting SSC of multiple pears. Artificial Intelligence in Agriculture, 16(1), 619-629. Link
• [10] Sun Z, Yang J, Zheng Y, Liu P, Ma C, Hu D, Ying Y, & Xie L. (2025). Explicable attention mechanism for diameter correction in predicting soluble solids content of fruits. Computers and Electronics in Agriculture, 239, 110848. Link
• [9] Ma Z, Peng B, Yue Z, Zeng H, Pan M, Yang J, Mai L, & Guan K (2025). Embracing large language model (LLM) technologies in hydrology research. Environmental Research: Water, 1(2), 022001. Link
• [8] Wang S, Chen C, Le X, Xu Q, Xu L, Zhang Y, & Yang J (2025). CAD-GPT: Synthesising CAD construction sequence with spatial reasoning-enhanced multimodal LLMs. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-25), 39(8), 7880-7888. Link
• [7] Sun Z, Yang J, Yao Y, Hu D, Ying Y, Guo J, & Xie L (2025). Knowledge-guided temperature correction method for soluble solids content detection of watermelon based on Vis/NIR spectroscopy. Artificial Intelligence in Agriculture, 15(1), 88-97. Link
• [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. (ESI Highly Cited Paper) 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 and Honors

• Impactful Research Award, University of Minnesota Postdoctoral Recognition Awards, 2024 (1 of 4 recipients university-wide).
• Session Convener, Sustainable Agriculture and Climate Change: Toward Decarbonization of Agrifood Systems, 2025 American Geophysical Union Annual Meeting (AGU25).
• Program Committee Member, 2026 AAAI Conference on Artificial Intelligence (AAAI-26).
• Manuscript Reviewer for peer-reviewed journals including Remote Sensing of Environment, Journal of Hydrology, Computers and Electronics in Agriculture, Journal of Field Robotics, Food Chemistry, Ecological Informatics, International Journal of Applied Earth Observation and Geoinformation, Hydrological Processes, Engineering Applications of Artificial Intelligence, and Smart Agricultural Technology.