Papers
11
Total Citations
154
H-Index
7
About
R. Masuda is a leading researcher in agricultural robotics, specializing in autonomous navigation, obstacle detection, and harvesting automation for combine harvesters. Their work centers on integrating deep learning, computer vision, and GPS-based control systems to create fully autonomous robotic harvesters capable of operating safely and efficiently in paddy fields. Masuda’s most impactful contribution is the implementation of a deep-learning algorithm for obstacle detection and collision avoidance in robotic harvesters (2020, 42 citations), significantly enhancing operational safety. They have also pioneered path-following control using RTK-GPS and GPS compass sensors, enabling precise autonomous navigation and turning (2013, 20 citations). Further, their development of image processing techniques for ridge/furrow discrimination and grain container searching (2013, 18 citations; 2014, 8 citations) has advanced field perception and unloading automation. Masuda’s recent work on real-time semantic segmentation for object detection (2023) continues to push the boundaries of safe, human-supervised robotic harvesting. With a cumulative citation count exceeding 150, their research is instrumental in addressing labor shortages and sustainability challenges in Japanese agriculture, laying the groundwork for next-generation smart farming systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Advanced Harvesting System by using a Combine Robot21 citations · 2013
- 3Path-Following Control of a Head-Feeding Combine Robot20 citations · 2013
- 4Remote Monitoring of Agricultural Robot using Web Application18 citations · 2013
- 5
- 6
- 7Efficient searching for grain storage container by combine robot8 citations · 2014
- 8
- 9Path Following Control for Head-feeding Combine Robot4 citations · 2013
- 10