Zijin Che

Institute of Agricultural Resources and Regional Planning

Papers

Total Citations

H-Index

About

Zijin Che is a leading researcher at the intersection of computer vision and precision agriculture, with a primary focus on developing intelligent detection systems for specialty crops. Their most cited work, "An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s" (2024, 16 citations), addresses a critical bottleneck in the global grape industry: rising labor costs and workforce shortages. Che’s major contribution lies in adapting the YOLOv5s deep learning architecture for unstructured orchard environments, enabling rapid and accurate identification of grapes—a foundational step for intelligent harvesting robots. By tackling the challenges of variable lighting, occlusion, and complex backgrounds inherent in real-world vineyards, this work provides a scalable solution that bridges the gap between computer vision algorithms and practical agricultural automation. Beyond this paper, Che’s research portfolio consistently explores the deployment of lightweight neural networks for real-time fruit detection, aiming to reduce computational overhead without sacrificing accuracy. Their achievements are particularly notable for addressing a pressing economic need: as labor shortages intensify globally, Che’s methods offer a pathway to cost-effective, autonomous harvesting. With growing citation momentum, their work is shaping the future of smart farming, making them a key voice in the field of agricultural robotics and deep learning applications.