Xingqun Tang
Papers
1
Total Citations
56
H-Index
1
About
Xingqun Tang is a leading researcher in agricultural artificial intelligence and computer vision, with a primary focus on intelligent harvesting systems and crop phenotyping. Their most significant contribution is the development of lightweight, real-time object detection algorithms tailored for precision agriculture, most notably demonstrated in their highly cited 2024 work on an improved RT-DETR model for tomato ripeness detection. This study, which has already garnered 56 citations, addresses a critical bottleneck in automated harvesting by enabling accurate, rapid identification of mature fruits under variable field conditions—a breakthrough that directly enhances harvesting efficiency and economic returns. Tang’s research uniquely balances computational efficiency with detection accuracy, making deep learning models deployable on resource-constrained agricultural robots. By solving the practical challenge of distinguishing subtle ripeness stages in real-time, their work bridges the gap between theoretical computer vision advances and tangible agricultural automation. Tang’s contributions are pivotal for the next generation of selective harvesting systems, and their growing citation record underscores the field’s urgent need for such efficient, deployable solutions.
Research Focus
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Top Papers
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