Papers
5
Total Citations
86
H-Index
4
About
Xinfa Wang is a computer vision and precision agriculture researcher whose work sits at the intersection of deep learning, object detection, and agricultural automation. Wang's most significant contribution is the development of SM-YOLOv5, a lightweight tomato fruit detection algorithm optimized for plant factories, robots, and mobile terminals — a paper that has garnered 58 citations since its 2023 publication, reflecting its rapid uptake within the intelligent agriculture community. Complementing this, Wang curated a dedicated tomato fruit image dataset tailored for complex lighting environments in plant factories, further supporting reproducible research in this domain with 15 citations to date. Beyond fruit detection, Wang has extended this expertise to livestock processing, proposing a lightweight saliency detection method for real-time bone localization in boning robots, addressing critical computational constraints in industrial settings. More recent contributions tackle nuanced challenges such as tomato ripeness classification, precise picking-point detection in greenhouse environments, and pomegranate growth-stage monitoring via a faster, deployment-ready YOLO variant. Collectively, Wang's research advances the practical deployment of AI-driven vision systems across diverse agricultural and food-processing contexts, making meaningful strides toward fully automated, intelligent farming operations.
Research Focus
Key Achievements
Top Papers
- 1Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory58 citations · 2023
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