Daniel Dooyum Uyeh

Daniel Dooyum Uyeh serves as an Assistant Professor in the Department of Biosystems and Agricultural Engineering at Michigan State University's College of Agriculture and Natural Resources since January 2023. His multidisciplinary research integrates computational intelligence, optical sensing, and system modeling to develop climate-smart agricultural solutions that enhance sustainability and resilience.

His academic foundation includes:

• Ph.D. in Bio-industrial Machinery Engineering (Kyungpook National University, 2019)
• M.A. in Food Security and Agricultural Development (Kyungpook National University, 2016)
• B.Sc. in Microbiology (University of Mkar, Nigeria, 2013)

Dr. Uyeh pioneers non-destructive sensing systems using near-infrared spectroscopy for rapid agricultural analysis, while his climate-smart decision support frameworks optimize resource efficiency in protected cultivation. His AI-driven modeling approaches target precision in horticulture and livestock systems, directly addressing environmental impact reduction and production cost minimization through data convergence across chemometrics, robotics, and computational intelligence.

His recognition as the 2024 Global Scholar in Research by MSU's College of Agriculture and Natural Resources highlights his impactful contributions to sustainable agricultural innovation.

As Principal Investigator of the Climate-Smart Decision Support Systems Group, he supervises undergraduate and graduate students in Agricultural Engineering and Food Security programs. His research leadership spans international collaborations with Kyungpook National University's Smart Agriculture Innovation Center and Upland-field Machinery Research Center, securing funding for sensor-based agricultural optimization projects while mentoring diverse student cohorts through inclusive educational practices.

His laboratory initiatives focus on portable chemometric tools for field-deployable agricultural diagnostics, advancing resilient food systems through real-time data integration and machine learning applications developed within MSU's interdisciplinary engineering-agriculture framework.