Yangwen Jin

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About

Yangwen Jin is a rising researcher at the forefront of agricultural and field robotics, with a primary focus on autonomous perception, navigation, and digital twinning for complex, unstructured environments. His work is distinguished by its integration of advanced computer vision and deep learning to solve practical challenges in horticulture. Jin’s most impactful contribution is the development of the YOLOv8n-DDA-SAM framework, a novel method for accurate cutting-point estimation in robotic cherry-tomato harvesting. This work, which has already garnered 14 citations since its 2024 publication, directly addresses a critical bottleneck in agricultural automation by enabling precise fruit localization and picking-point identification. He has also pioneered a context-aware navigation framework for ground robots in horticultural settings, integrating LiDAR SLAM with semantic mapping to achieve robust autonomous movement in cluttered gardens. Further expanding his scope, Jin is exploring autonomous digital modelling for unknown environments, proposing an innovative solution for robot odometry and exploration to support Digital Twinning in wild, unstructured scenes. Through these contributions, Jin is establishing himself as a key innovator in creating intelligent, autonomous systems that can operate reliably in the demanding conditions of modern agriculture and field robotics.