Papers
2
Total Citations
26
H-Index
2
About
Qiyan Sun is an emerging researcher specializing in computer vision, deep learning, and precision agriculture, with a particular focus on deploying intelligent detection systems on resource-constrained embedded devices. Sun's most notable contribution centers on the development of a lightweight, high-precision passion fruit detection model built upon a modified YOLOv5 architecture. This work addresses the practical challenges of real-world agricultural environments — including backlighting, occlusion, object overlap, and varying weather conditions — by redesigning the backbone network to balance computational efficiency with detection accuracy. The research demonstrates a meaningful commitment to bridging the gap between advanced machine learning models and the hardware limitations of field-deployable devices, a critical consideration for smart farming applications. Sun's passion fruit detection model has garnered approximately 26 combined citations across related publications in 2024 alone, reflecting strong early-career impact and relevance within the agricultural AI community. This work positions Sun as a promising contributor to the intersection of embedded systems and agricultural automation, with implications for improving crop monitoring, yield estimation, and harvesting efficiency in fruit farming industries worldwide.
Research Focus
Key Achievements
Top Papers
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