Jae Ho Kim

Papers

Total Citations

435

H-Index

About

Jae Ho Kim is a leading researcher in agricultural robotics and computer vision, specializing in deep learning-based fruit detection for automated harvesting systems. His work directly addresses the critical challenge of enabling robots to accurately identify and locate fruits in complex, natural environments where variable lighting, foliage occlusion, and fruit overlap hinder performance. Kim’s major contributions center on developing robust, real-time detection algorithms built on the YOLO (You Only Look Once) framework. His highly influential 2020 paper, "YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3," has garnered 414 citations, establishing a foundational approach for the field. Building on this, his more recent 2024 work, "Accurate and fast detection of tomatoes based on improved YOLOv5s in natural environments," introduces the SBCS-YOLOv5s model, achieving superior accuracy and speed for practical deployment. With a growing citation impact, Kim’s research is pivotal for advancing the viability of harvesting robots, directly contributing to the future of precision agriculture and sustainable food production.