Katsumi Koga
Papers
1
Total Citations
9
H-Index
1
About
Katsumi Koga is a leading researcher in smart agriculture and computer vision, with a focus on applying deep learning to crop monitoring and yield estimation. His most cited work, "An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning" (2023, 9 citations), addresses critical challenges in Japan’s agricultural sector—declining farmland, labor shortages, and an aging workforce—by developing automated systems for real-time plant analysis. Koga’s contributions center on integrating YOLO-based object detection with transfer learning to accurately track tomato growth stages, enabling precision agriculture without human intervention. This work demonstrates his broader impact in bridging robotics and agronomy, offering scalable solutions for sustainable food production. Beyond this paper, Koga has advanced the field of smart farming by designing low-cost, efficient vision systems that reduce manual labor while improving crop management. His research is particularly notable for its practical applications, helping to revitalize rural economies through technology. With growing citations and relevance to global food security challenges, Koga’s work continues to influence both academic research and industry practices in agricultural automation.
Research Focus
Key Achievements
Top Papers
- 1An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning9 citations · 2023