Tackle Big Challenges with Innovation


Feeding the growing global population is challenging, especially when there are increasing competitions for land and water to maintain other essential ecosystem services. Our research thus integrates ecology, computational modeling, remote sensing, and machine learning approaches to advance the science that guides sustainable agricultural management, and to develop tools that can help farmers and regulators apply this science more effectively. 

With the rapid progress in earth observatory power and a range of modern tools, we're particularly interested: (1) remote sensing and deep learning for agriculture management for mapping agriculture elements and patterns; (2) scalable quantification technology for high-resolution greenhouse gas (GHGs) fluxes; (3) process-based hybrid modeling and knowledgeth-guided machine learning for agroecosystem prediction; (4) understanding the impacts of climate change on agroecosystem; (5) controlled environment and urban agriculture


Two postdoc researcher positions are available through the Department of Biosystems and Bioproducts Engineering this year. Researches in the lab are fairly interdisciplinary, thus students with backgrounds related to quantitative modeling in ecology and agriculture, environmental science, remote sensing, GIS, statistics, and computer science are all welcome. Please send your CV and research interest to jinzn@umn.edu to discuss possibilities.


04/2022 Our group has published several high impact studies, and all of them have been picked up by UMN news, EurekAlert!, ScienceDaily, and more.
A Nature Climate Change paper led by Zhenong Jin: Critical benefits of snowpack for winter wheat are diminishing
A Geoscientifc Model Developement paper led by Licheng Liu: New study could help reduce agricultural greenhouse gas emissions
A Remote Sensing of Environment paper led by Chenxi Lin: Using technology to identify crop types early in the season, without entering the field

01/2022 In partnership with Changhyun Choi's group from ECE, we received a Seed Grant from Minnesota Robotics Institute to automate indoor strawberry yield prediction and harvesting!

12/2021 Yufeng Yang has been awarded the very selective UMII-MnDRIVE fellowship for 2022-2023. Congratulations!

11/2021 Our group has started two new projects to work on building smart and connected nutrient management communities (funded by the NSF S&CC program #2125626) and understanding and mitigating turfgrass winter stresses (funded by USDA SCRI #2021-51181-35861)!