Papers
1
Total Citations
28
H-Index
1
About
Zuoxun Wu is a leading researcher in agricultural robotics and computer vision, with a primary focus on intelligent fruit recognition and automated harvesting systems. His most impactful work centers on developing efficient, deep-learning-based methods for real-time fruit detection and three-dimensional localization in complex agricultural environments. Wu’s landmark study, “Tomato Recognition and Localization Method Based on Improved YOLOv5n-seg Model and Binocular Stereo Vision” (2023), has garnered 28 citations for its innovative solution to two critical challenges: the high computational cost of neural-network-based fruit recognition and the low accuracy of traditional stereo matching algorithms. By optimizing the YOLOv5n-seg architecture and integrating it with binocular stereo vision, Wu significantly reduced model complexity while enhancing localization precision—a breakthrough that directly advances the feasibility of automated fruit picking. His work bridges the gap between lightweight AI models and practical agricultural applications, offering a scalable approach for real-time, high-accuracy harvesting. Wu’s contributions are pivotal for researchers and engineers seeking to deploy vision-guided robotic systems in unstructured field conditions, making him a key figure in the intersection of precision agriculture and embedded AI.
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Top Papers
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