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About

Tantan Jin is a leading researcher at the intersection of precision agriculture and robotics, with a primary focus on developing intelligent robotic systems for fruit harvesting. Their work centers on integrating advanced computer vision, real-time perception, and autonomous path planning to overcome the labor-intensive challenges of modern orchards. Jin’s most impactful contribution is a comprehensive review of robotic arms in precision agriculture, which has garnered 125 citations and serves as a foundational resource for the field. Building on this, Jin developed an enhanced deep learning model—an optimized YOLOv8n architecture—that significantly improves apple detection, localization, and counting in complex orchard environments, outperforming standard models like YOLOv5 and Real-Time Detection Transformer. This work directly enables high-precision robotic harvesting. Further demonstrating practical application, Jin designed and evaluated a 6-DOF robotic arm system with integrated real-time perception and path planning for dwarf hedge-planted apple orchards, achieving robust performance in dynamic conditions. Through these contributions, Jin is advancing the frontier of autonomous agriculture, making robotic harvesting more efficient, reliable, and scalable for the future of food production.