Shanda Li

Guangxi University of Science and Technology

Papers

Total Citations

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About

Shanda Li is a researcher at the forefront of agricultural robotics and computer vision, specializing in deep learning solutions for precision agriculture. Her work focuses on developing lightweight, real-time detection and segmentation models that enable robots to operate effectively in complex, unstructured orchard and field environments. Li’s most cited paper, “MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3” (2023, 35 citations), addresses the critical challenge of rapid and accurate mushroom detection for robotic harvesting, overcoming issues like diverse morphology and dense shading. Her second highly influential work, “Citrus Tree Crown Segmentation of Orchard Spraying Robot Based on RGB-D Image and Improved Mask R-CNN” (2022, 20 citations), tackles variable growth-stage-based spraying by improving crown segmentation accuracy in complex orchard settings. Together, these contributions demonstrate Li’s impact in bridging state-of-the-art computer vision with practical agricultural automation, providing robust, efficient solutions that directly enhance robotic perception for harvesting and spraying tasks. Her research is essential reading for anyone interested in applying deep learning to real-world agricultural challenges.