Xinglin Jin
Papers
1
Total Citations
5
H-Index
1
About
Xinglin Jin is a researcher at the forefront of precision agriculture and computer vision, with a primary focus on intelligent weed detection for upland rice systems. His most notable contribution is the development of PHRF-RTDETR, a lightweight and efficient weed detection method introduced in his 2025 paper, which has already garnered 5 citations. This work directly addresses a critical challenge: while weeds pose a greater threat to rice yield and quality in upland environments compared to paddy fields, existing detection methods often fail to balance accuracy with computational efficiency. By innovating upon the RT-DETR architecture, Jin has advanced the practical application of deep learning for real-time, field-deployable weed control, a key step toward sustainable, automated agriculture. His research bridges the gap between cutting-edge AI and agronomic needs, offering a scalable solution that reduces reliance on herbicides. With a growing citation impact, Jin’s work is shaping the future of smart farming, making him a rising voice in agricultural technology and computer vision.
Research Focus
Key Achievements
Top Papers
- 1