Xiaojun Jin

Papers

Total Citations

134

H-Index

About

Xiaojun Jin is a pioneering researcher in precision agriculture and agricultural robotics, with a focus on intelligent crop management and harvesting systems. Her work spans two critical areas: deep learning-based weed detection and automated tea harvesting. In her most impactful study, "A novel deep learning-based method for detection of weeds in vegetables" (2022, 116 citations), she developed a groundbreaking approach that enables rapid, accurate weed identification in complex vegetable fields—addressing a major challenge in sustainable farming by reducing herbicide use. This work has become a key reference in precision weed control. Earlier, Jin laid foundational contributions to tea harvesting robotics. Her research on "Tea Flushes Identification Based on Machine Vision" (2013) established algorithms for distinguishing tender tea shoots from natural backgrounds, while her "Parallel Robot for Tea Flushes Plucking" (2015) introduced a novel robotic mechanism for selective, high-quality tea harvest. These innovations directly address the labor shortages and quality demands in premium tea production. With her deep learning work garnering over 100 citations, Jin is recognized for bridging computer vision and agricultural engineering, advancing both the science and practice of smart farming.