Jianping Jing
Papers
3
Total Citations
33
H-Index
3
About
Jianping Jing is a leading researcher in agricultural robotics and intelligent fruit detection, specializing in deep learning-based computer vision for complex orchard environments. Their major contributions center on developing lightweight, high-precision object detection models that enable automated fruit thinning and harvesting—critical technologies for reducing labor costs and improving crop quality. Jing’s flagship work, “Intelligent Detection of Lightweight ‘Yuluxiang’ Pear in Non-Structural Environment Based on YOLO-GEW” (2023, 17 citations), introduces a YOLOv8s-derived model using GhostNet to overcome challenges like fruit-leaf color similarity and bagging. This is complemented by “YOLO-PEM: A Lightweight Detection Method for Young ‘Okubo’ Peaches” (2024, 11 citations), which achieves automatic detection of immature peaches for robotic thinning. Jing further refines maturity assessment with “Detection of maturity of ‘Okubo’ peach fruits based on inverted residual mobile block and asymptotic feature pyramid network” (2024, 5 citations). Collectively, these works demonstrate Jing’s impact in advancing efficient, real-time AI solutions for precision agriculture, with growing citation counts reflecting their relevance to sustainable farming technology.
Research Focus
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Top Papers
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