AI Soybean Yield Prediction Brazil: Transfer Learning Boosts Accuracy | AcademicJobs
Explore how UIUC's AI transfer learning revolutionizes Brazil soybean yield prediction, boosting accuracy for CONAB forecasts and ag decisions.

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Jiaying Zhang is a Research Scientist at the University of Illinois at Urbana-Champaign, affiliated with the Agroecosystem Sustainability Center and the Institute for Sustainability, Energy, and Environment. She holds a Doctor of Philosophy degree. Her research focuses on agricultural modeling, including the use of artificial intelligence, satellite observations, climate data, and transfer learning techniques to predict crop yields at various scales, with recent work addressing soybean production in Brazil.
Zhang has authored or co-authored numerous peer-reviewed publications. Key works include studies on forest structure and hurricane impacts published in 2022, evaluations of precipitation products in 2018, and papers on aligning satellite-based phenology in deep learning models for crop yield estimates in 2025. Additional publications cover seasonal climate forecasts over South America in 2023 and related topics in hydrometeorology and geoscientific modeling. Her contributions appear in journals such as Agricultural and Forest Meteorology, Journal of Hydrometeorology, and International Journal of Applied Earth Observation and Geoinformation. Zhang’s professional email address is zhjiay5@illinois.edu.
Explore how UIUC's AI transfer learning revolutionizes Brazil soybean yield prediction, boosting accuracy for CONAB forecasts and ag decisions.