Guo‐Qiang Bao
Papers
1
Total Citations
2
H-Index
1
About
Guo-Qiang Bao is a researcher at the forefront of agricultural AI, specializing in lightweight object detection for precision farming. His work focuses on developing efficient, real-time computer vision models that can operate under the complex, variable conditions of natural environments—a critical challenge for automated crop monitoring and harvesting. Bao’s major contribution is the DSW-YOLO model, an innovative improvement on the YOLOv10n architecture. He designed the novel DWRR block to enhance feature extraction while keeping the model lightweight, enabling accurate detection of green peppers amidst dense foliage, varying light, and occlusions. This work, published in 2025, has already garnered 2 citations, signaling its immediate relevance to the agricultural robotics community. By systematically comparing mainstream lightweight detectors, Bao demonstrated a rigorous methodology that ensures his model is both high-performing and deployable on resource-constrained devices. His research directly addresses the industry’s need for fast, reliable, and compact vision systems, paving the way for smarter, more autonomous agricultural practices.
Research Focus
Key Achievements
Top Papers
- 1DSW-YOLO-Based Green Pepper Detection Method Under Complex Environments2 citations · 2025