Chengyue Ji
Papers
1
Total Citations
20
H-Index
1
About
Chengyue Ji is a researcher focused on the intersection of computer vision and agricultural automation, with a particular emphasis on lightweight object detection for precision livestock farming. Their most notable contribution is the development of the improved YOLOv8-PG model, a specialized deep learning architecture designed to address the critical challenge of distinguishing real from fake pigeon eggs in commercial egg-producing farms. This work directly tackles the high egg breakage rates and significant labor costs plaguing the industry by optimizing the YOLOv8n backbone—specifically modifying the Bottleneck within the C2f module—to create a more efficient, lightweight detection system. The model, detailed in their 2024 paper which has already garnered 20 citations, demonstrates Ji’s ability to bridge the gap between state-of-the-art AI and practical, real-world agricultural needs. Their research not only advances the field of agricultural robotics but also sets a precedent for deploying resource-efficient neural networks in resource-constrained farming environments, marking Ji as an emerging innovator in smart agriculture.
Research Focus
Key Achievements
Top Papers
- 1Improved YOLOv8 Model for Lightweight Pigeon Egg Detection20 citations · 2024