Ayanori Yorozu
Papers
2
Total Citations
37
H-Index
2
About
Ayanori Yorozu is an emerging researcher whose work sits at the intersection of agricultural robotics, computer vision, and motion planning, with a particular focus on automating the harvesting of winter jujube in complex orchard environments. Their research addresses one of precision agriculture's most pressing challenges: enabling robots to reliably detect, localize, and harvest delicate fruits in unstructured, real-world conditions. Yorozu's most impactful contribution to date is the development of MLG-YOLO, a real-time detection and localization model capable of identifying winter jujubes with spatial accuracy within approximately 3.9 to 4.7 mm across multiple directions — a level of precision critical for robotic harvesting applications. This work has already garnered 19 citations since its 2024 publication, reflecting rapid uptake within the agricultural robotics community. Complementing this, Yorozu's optimized Informed-RRT* motion planning algorithm significantly reduces path length and planning time for harvesting robotic arms, while maintaining stability across dynamic 3D environments — earning 18 citations in the same year. Together, these contributions establish Yorozu as a promising voice in intelligent agricultural automation, offering practical, technically rigorous solutions that bring fully autonomous fruit-harvesting robots meaningfully closer to reality.
Research Focus
Key Achievements
Top Papers
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