Papers
2
Total Citations
93
H-Index
2
About
Zuolin Li is a leading researcher in agricultural robotics and computer vision, with a focus on intelligent monitoring and automation in greenhouse environments. Their work centers on developing deep learning models for detecting, tracking, and counting crops at various growth stages—critical for yield prediction and robotic pollination. Li’s most cited paper (76 citations) introduces an improved YOLO-Deepsort network that enables inspection robots to track and count tomatoes across different growth periods, integrating ShuffleNetV2 and CBAM attention mechanisms for enhanced efficiency. Another notable contribution (17 citations) proposes a neural network with attention and feature fusion layers to detect tomato flowering phases, addressing challenges like complex lighting to support pollination robots. These innovations demonstrate Li’s impact on precision agriculture, bridging computer vision and robotics to solve real-world farming challenges. Their work is widely cited by researchers in agricultural automation and smart greenhouse technology, highlighting its practical significance.
Research Focus
Key Achievements
Top Papers
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