Xiaoshi Shi
Papers
2
Total Citations
79
H-Index
2
About
Xiaoshi Shi is a leading researcher in agricultural artificial intelligence, specializing in real-time fruit detection and computer vision for precision agriculture. Dr. Shi’s major contributions center on developing high-performance, lightweight deep learning models that can accurately identify and localize citrus fruits in complex, unstructured orchard environments. The researcher’s seminal work, “Real-time and accurate detection of citrus in complex scenes based on HPL-YOLOv4” (2022), has garnered 67 citations, establishing a foundational approach for robust fruit detection under challenging lighting and occlusion conditions. Building on this, Dr. Shi introduced YOLOC-tiny (2024), a generalized lightweight model specifically designed to address the critical challenge of detecting large, non-green-ripe citrus fruits across multiple ripeness stages and varieties. This innovative architecture, built upon YOLOv7 with efficient components, achieves high precision while maintaining real-time performance, directly supporting automated harvesting and yield estimation. Dr. Shi’s work bridges the gap between state-of-the-art object detection and practical agricultural needs, enabling smarter, more efficient farming solutions.
Research Focus
Key Achievements
Top Papers
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