Papers
9
Total Citations
346
H-Index
9
About
Dr. Xiujuan Chai is a leading researcher in agricultural robotics and intelligent vision systems, whose work is transforming precision agriculture. Her primary research areas encompass deep learning for crop and weed detection, autonomous harvesting, and digital twin technology for smart farming. Dr. Chai’s most impactful contribution is her pioneering work on weed detection in cotton fields, where her team developed the YOLO-WDNet model—a lightweight, high-accuracy system for real-time, targeted spraying. This work, published in 2023 and 2024, has garnered over 180 citations, underscoring its significance in reducing herbicide use. She has also made substantial advances in fruit harvesting automation, notably developing and field-evaluating an autonomous citrus-harvesting robot (48 citations) and creating a novel RGB-based citrus pose estimation method (37 citations) that enables precise robotic grasping. Her innovative application of attention-guided networks for apple bud-type classification and the development of a digital twin-driven system for greenhouse tomato harvesting further demonstrate her versatility. Beyond agriculture, Dr. Chai has applied deep visual features to library book segmentation and recognition. With over 300 total citations, her work is driving the next generation of efficient, autonomous agricultural systems.
Research Focus
Key Achievements
Top Papers
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- 4Citrus pose estimation from an RGB image for automated harvesting37 citations · 2023
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- 8Digital twin-driven system for efficient tomato harvesting in greenhouses14 citations · 2025
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