Min-Ho Jang

Papers

Total Citations

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About

Min-Ho Jang is a researcher at the forefront of agricultural robotics and computer vision, with a specialized focus on precision harvesting systems. His most-cited work, "Tomato pose estimation using the association of tomato body and sepal" (2024, 16 citations), introduces a novel deep learning approach that accurately estimates the 3D orientation of tomatoes by jointly detecting the fruit body and its sepal. This contribution directly addresses a critical bottleneck in robotic fruit picking—enabling robots to grasp tomatoes without damaging the stem or fruit. By leveraging geometric associations between plant parts, Jang’s method improves pose estimation accuracy under occluded and cluttered field conditions, a common challenge in greenhouse environments. His research bridges the gap between agricultural engineering and artificial intelligence, offering scalable solutions for automated harvesting. The work has already influenced subsequent studies in fruit detection and manipulation, demonstrating its practical impact. Jang’s achievements highlight his ability to solve real-world agricultural problems through innovative computer vision techniques, making him a rising figure in the field of smart farming and robotic perception.