Shuping Tang
Papers
2
Total Citations
62
H-Index
2
About
Shuping Tang’s research lies at the intersection of agricultural robotics and machine vision, with a focus on automating the harvesting of high-value crops. Her most cited work, “A method of segmenting apples at night based on color and position information” (2016, 53 citations), addresses a critical challenge in precision agriculture: enabling fruit-picking robots to operate reliably in low-light conditions. By integrating color and spatial data, Tang developed a segmentation technique that improves detection accuracy, directly supporting round-the-clock harvesting efficiency. In a related study on lotus picking robots (2016, 9 citations), she proposed a novel feature extraction method combining shape analysis with machine learning, tackling the difficult task of recognizing irregular, partially occluded produce in complex field environments. These contributions have helped advance the key link of image segmentation and recognition in agricultural robotics, laying groundwork for more autonomous, adaptive harvesting systems. Tang’s work is particularly notable for its practical orientation—bridging computer vision theory with real-world constraints like variable lighting and crop morphology—making her a valuable voice in the growing field of smart farming technology.
Research Focus
Key Achievements
Top Papers
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