By Kathryn Kendall
Modern agriculture generates more data than ever before from various sources such as drones, satellites, weather stations, and sensors. Much of this information remains disconnected, underutilized, or too complex to inform real time decisions. Producers often struggle to turn these multiple and complex datasets into practical tools that improve crop performance and sustainability. As part of Michigan State University's goal to provide leading technologies and solutions, Anjin Chang, Ph.D., joins the Department of Biosystems and Agricultural Engineering. He is addressing this challenge by developing data-driven smart agriculture systems that integrate diverse data sources and transform them into intuitive decision-making support tools.
Over the past decade, Chang has specialized in high-quality imaging technologies, leveraging data collected from drones, satellites, and image sensing systems. By monitoring crops throughout the growing season, his research applies artificial intelligence and advanced modeling techniques to analyze plant health, predict outcomes, and support timely management decisions.
“For data-driven agricultural applications and practices, advanced analytics and artificial intelligence are only as powerful as the data behind them.” said Chang “If data are noisy, inconsistent, or poorly collected, even the most sophisticated models can produce misleading results. My research emphasizes collecting high-quality data and establishing standardized data-processing frameworks for smart agriculture, because reliable data are the foundation for trustworthy decision-making tools that producers can confidently use in real-world agricultural systems.”
Source : msu.edu