Assistant Professor, Computational Agricultural & Environmental Sciences

Katsutoshi “Toshi” Mizuta, Ph.D., is an assistant professor who is interested in precision agriculture and digital soil mapping with a particular focus on soil health, climate-smart agriculture and ecosystem services using machine learning algorithms. His expertise spans various areas, from conducting lab-based biogeochemical incubation studies to utilizing proximal, airborne and satellite remote sensing technologies for a large scale research based on big data of soil, crop, biome, climate, terrain, geology and human being. He is also dedicated to developing decision-making support tools for cost-effective and sustainable management practices concerning global food security and climate change. The highly interdisciplinary nature of his work has led to collaborations with various scientists, companies and government agencies, which has resulted in 12 publications, six conference proceedings, 30+ extension reports, 50+ conference presentations and 20+ awards. He received his B.S. in environmental engineering in 2014 from the Soka University in Japan. He received both his M.S. majoring in soil and water sciences (SWS) in 2016 and his Ph.D. majoring in SWS (minoring in food and resource economics). He completed his postdoctoral position at University of Minnesota Precision Agriculture Center during 2021-23. He was an assistant professor at Ohio Wesleyan University before he joined UK.
Toshi is interested in extending his collaborations through interdisciplinary projects in research, teaching and services to improve current agricultural production systems agronomically, economically, ecologically and sustainably.
His current teaching:
- PLS 103H: Plants, Soils, and People: a Global Perspective
(3credits) every spring
- PLS 597 Precision management for agriculture and natural resources conservation for undergrad and grad students (3 credits) every fall
- AES 395: Independent Research in Plant and Soil Sciences or Agricultural Ecosystem Sciences (Lab) 1-4 Credits, every Spring, Summer, and Fall
- AES 399: Experimental Learning in Plant and Soil Science or Agricultural Ecosystem Sciences (Field) 1-4 Credits, every Spring, Summer, and Fall

Areas of Interest

Precision agriculture, artificial intelligence (machine learning, deep learning), pedometrics (digital soil mapping, geospatial modeling), proximal soil and plant proximal sensing (visible/near-infrared and mid-infrared spectroscopy, pXRF), aerial (optical/thermal/multi-spectrum) and satellite remote sensing, soil health and econometrics