AI Breakthrough Boosts Brazil Soybean Yield Forecasting

New AI-based system can generate high-resolution soybean yield maps across Brazil using limited local data.

Published on Feb. 13, 2026

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Researchers at the University of Illinois Urbana-Champaign have developed a new AI-based system that can generate high-resolution soybean yield maps across Brazil using only limited local data. This innovative approach enables high-performance national yield estimates for Brazilian soybeans, even in areas where directly reported local yield data are very limited. By leveraging knowledge learned from earlier U.S.-based work through 'AI transfer learning,' the research team was able to make detailed yield predictions at the municipal level using Brazil's state-level soybean yield data.

Why it matters

Brazil is currently the world's largest soybean producer and a major global food exporter, but high-resolution yield data for Brazilian soybeans remain largely unavailable. These data are essential for precision agriculture, risk management, and sustainability planning. The new AI-based system addresses this critical global data gap and provides a pathway for applying advanced yield modeling in regions of the world with limited data, supporting food security planning, climate risk management, and evidence-based agricultural policy.

The details

The researchers developed a new framework to predict national soybean yields at a finer level by integrating satellite observations, climate data, and state-level yield statistics, leveraging AI transfer learning techniques with the knowledge learned from their U.S. based models. The model for Brazilian soybean achieved strong predictive performance without using any municipal-level yield data, and performance improved further when municipal data were included. This approach boosted the effectiveness of cross-scale yield prediction from 50 percent to 78 percent of the theoretical upper limit.

  • The study was published in February 2026.

The players

University of Illinois Urbana-Champaign

A public research university located in Urbana, Illinois, and the institution where the researchers who developed the new AI-based system are based.

Kaiyu Guan

The project lead and senior author of the study, who is the Levenick Endowed Professor and Director of the Agroecosystem Sustainability Center at the University of Illinois Urbana-Champaign.

Jiaying Zhang

The first author of the study, who explained the key innovation of the research using AI transfer learning.

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What they’re saying

“This approach boosted the effectiveness of cross-scale yield prediction from 50 percent to 78 percent of the theoretical upper limit, which we defined as the best performance achieved by models trained with highly detailed local yield data. The results demonstrate that AI-driven transfer learning can overcome both data scarcity and scalability challenges in agricultural modeling.”

— Jiaying Zhang, First author (International Journal of Applied Earth Observations and Geoinformation)

“The ability to monitor and anticipate crop production regionally and globally with high fidelity is strategically important for market analysis, trade forecasting, and risk assessment for U.S. soybean producers.”

— Kaiyu Guan, Levenick Endowed Professor and Director of the Agroecosystem Sustainability Center (International Journal of Applied Earth Observations and Geoinformation)

What’s next

The researchers plan to further refine and expand the AI-based system to provide even more detailed and accurate soybean yield forecasts for Brazil and other major agricultural regions around the world.

The takeaway

This study demonstrates how advanced AI and transfer learning techniques can be leveraged to overcome data scarcity challenges and generate high-resolution agricultural intelligence, which is crucial for supporting global food security, sustainable farming practices, and informed decision-making in the face of climate change and other environmental pressures.