Keita Fukada

Tokyo University of Agriculture and Technology

Papers

Total Citations

H-Index

About

Keita Fukada is a rising researcher at the forefront of smart agriculture, specializing in computer vision and deep learning for automated crop monitoring. His work directly addresses critical challenges in Japan’s agricultural sector, including labor shortages and an aging farming population. Fukada’s most cited paper, “An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning” (2023, 9 citations), introduces a novel approach to real-time plant phenotyping. By leveraging YOLO-based transfer learning, he developed a system capable of automatically detecting and analyzing tomato growth stages—such as flowering and fruit ripening—with high accuracy. This contribution is pivotal for enabling robotic harvesting and precision agriculture, reducing reliance on manual observation. Though early in his career, Fukada’s work has already demonstrated significant potential for scalable, low-cost automation in greenhouse environments. His research bridges the gap between state-of-the-art object detection algorithms and practical agricultural needs, offering a pathway toward more efficient, data-driven farming. As smart agriculture continues to evolve, Fukada’s innovations in growth analysis systems position him as a key contributor to the future of sustainable food production.