Papers
1
Total Citations
9
H-Index
1
About
Liping Mo is a researcher advancing the field of agricultural robotics through deep learning and computer vision. Her primary research focuses on intelligent harvesting systems, particularly the design of detection and localization algorithms for fruit-picking robots. Mo’s most notable contribution is her work on a tomato picking robot system that integrates the YOLOv5 deep learning neural network with the Semi-Global Block Matching (SGBM) algorithm. This approach significantly improves detection accuracy and spatial localization, addressing critical challenges in automated agriculture. Her 2025 paper on this system has already garnered 9 citations, reflecting its timely relevance in the push toward smart farming and production efficiency. By combining state-of-the-art object detection with stereo vision, Mo’s research offers a practical pathway for enhancing the precision and reliability of robotic harvesters. Her work stands at the intersection of artificial intelligence and agricultural engineering, contributing to the broader goal of reducing labor dependency and increasing crop yield through automation. For students and researchers interested in applied deep learning or agricultural robotics, Mo’s studies provide a clear example of how neural networks can be tailored for real-world, high-impact tasks.
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