Papers
6
Total Citations
270
H-Index
6
About
Tan Sun is a leading researcher in agricultural robotics and computer vision, whose work is transforming precision farming and automated harvesting. His primary contributions lie in developing deep learning models for real-time weed detection and robotic intervention, as exemplified by his highly cited 2023 paper on a deep learning-based weed detection and target spraying robot for cotton fields (104 citations). Sun further advanced this field with YOLO-WDNet, a lightweight yet accurate weed detection model for cotton (78 citations, 2024), demonstrating a commitment to deployable, efficient AI. His research extends to fruit and crop automation, including citrus pose estimation for robotic harvesting (37 citations) and a digital twin-driven system for efficient greenhouse tomato harvesting (2025). Sun has also innovated in agricultural phenotyping with an attention-guided network for apple bud-type classification (19 citations) and applied computer vision to library automation with on-shelf book segmentation (18 citations). With over 270 total citations, Tan Sun’s work bridges cutting-edge deep learning and practical agricultural challenges, making him a key figure in smart farming and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3Citrus pose estimation from an RGB image for automated harvesting37 citations · 2023
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- 6Digital twin-driven system for efficient tomato harvesting in greenhouses14 citations · 2025