Xinjie Tan
Papers
1
Total Citations
4
H-Index
1
About
Xinjie Tan is a researcher at the forefront of agricultural AI and computer vision, with a focus on intelligent fruit detection and segmentation in complex natural environments. Their most-cited work, "Enhancing green guava segmentation with texture consistency loss and reverse attention mechanism under complex background" (2025), introduces a novel deep learning framework that addresses a critical challenge in precision agriculture: accurately segmenting green fruits that visually blend into foliage. By integrating a texture consistency loss function with a reverse attention mechanism, Tan’s method significantly improves segmentation accuracy under challenging conditions such as occlusion, varying illumination, and dense backgrounds. This contribution has already garnered 4 citations, signaling its early impact on the field. Tan’s research bridges the gap between advanced neural network architectures and practical agricultural applications, offering scalable solutions for automated harvesting and yield estimation. Their work is particularly notable for its focus on green fruit detection—a notoriously difficult problem due to color similarity with leaves—making it a valuable resource for researchers and engineers developing smart farming technologies.
Research Focus
Key Achievements
Top Papers
- 1