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About

Jinlin Xue is a prominent researcher specializing in agricultural robotics, machine vision, and autonomous navigation systems for precision agriculture. His work spans two decades of innovation aimed at automating labor-intensive farming operations, from row crop navigation to orchard fruit picking. Xue's most influential contribution, "Variable Field-of-View Machine Vision Based Row Guidance of an Agricultural Robot" (2012, 145 citations), established a foundational framework for vision-guided autonomous navigation in row crops, introducing adaptive camera control to handle complex field environments. His early work on autonomous agricultural robot platforms and headland turning further demonstrated practical, deployable solutions for real-world farming conditions. More recently, Xue has turned his attention to orchard automation, developing YOLO-P (2023, 53 citations), an efficient deep learning-based method for detecting pears under challenging lighting and occlusion conditions. Complementary work on binocular vision-based target ranging and LiDAR-vision data fusion for trunk detection reflects his commitment to robust, multi-sensor perception systems. Across his portfolio, Xue has also advanced path-following control using sliding mode variable structure methods and designed multipurpose greenhouse robots capable of spraying and weeding. With over 300 cumulative citations, his research meaningfully bridges robotics engineering and practical agricultural automation, making him a valuable reference for students entering the field of agri-robotics.