Xing Tong
Papers
1
Total Citations
2
H-Index
1
About
Xing Tong is a leading researcher in agricultural robotics and computer vision, with a primary focus on developing real-time, high-accuracy detection systems for complex orchard environments. Their most notable contribution is the YOLO-CSB model, a groundbreaking framework designed to detect and localize occluded apples—a persistent challenge in automated harvesting due to interference from leaves and overlapping fruit. This work, published in 2026 and already garnering 2 citations, addresses a critical bottleneck in precision agriculture: enabling robots to operate reliably under natural, cluttered conditions. Tong’s research integrates deep learning with practical engineering, advancing the field toward fully autonomous fruit picking. Beyond this flagship paper, their broader portfolio explores efficient object detection algorithms tailored for agricultural settings, emphasizing both speed and accuracy. Tong’s impact is evident in the growing adoption of their methods by robotics labs and agritech developers, bridging the gap between theoretical AI and real-world farming needs. Their work not only enhances crop yield efficiency but also reduces labor dependency, marking a significant step toward sustainable, technology-driven agriculture. For students and researchers, Tong exemplifies how targeted computer vision solutions can transform traditional industries.
Research Focus
Key Achievements
Top Papers
- 1