Xuebin Jing
Papers
1
Total Citations
24
H-Index
1
About
Xuebin Jing is a researcher at the forefront of applying deep learning to agricultural automation, with a particular focus on fruit phenotyping and precision farming. His work addresses the critical challenge of developing efficient, lightweight computer vision models for resource-constrained environments, such as edge devices used in fields. His most cited paper, "Melon ripeness detection by an improved object detection algorithm for resource constrained environments" (2024), has already garnered 24 citations, highlighting its immediate impact. In this work, Jing proposed a novel lightweight detection algorithm that significantly enhances the speed and accuracy of ripeness assessment, moving beyond costly manual methods. This contribution is vital for enabling real-time, on-device analysis in smart agriculture. By optimizing deep learning architectures for limited computational power, Jing is helping to democratize advanced AI tools for farmers and agronomists, paving the way for more efficient harvesting and reduced post-harvest losses. His research sits at the intersection of computer vision, embedded systems, and sustainable agriculture.
Research Focus
Key Achievements
Top Papers
- 1