Pengjun Xiang
Papers
2
Total Citations
6
H-Index
2
About
Pengjun Xiang is a pioneering researcher at the intersection of agricultural robotics and computer vision, with a primary focus on developing intelligent systems for automated crop harvesting and quality assessment. His most impactful work introduces FFTCA, a novel feature fusion mechanism leveraging Fast Fourier Transform that enables rapid classification of apple damage and real-time robotic sorting, already garnering 4 citations since its 2024 publication. Building on this foundation, Xiang's MixSegNext model represents a breakthrough in semantic segmentation for precision agriculture, combining CNN-Transformer hybrid architectures to accurately localize picking points for Sichuan pepper in complex natural environments. This 2025 work demonstrates his commitment to solving real-world agricultural challenges through deep learning innovations. Xiang's contributions are particularly notable for their practical applications in reducing post-harvest losses and enabling autonomous harvesting systems, with his algorithms showing exceptional performance in variable lighting and occluded conditions typical of field environments. His research bridges the gap between theoretical computer vision advances and deployable agricultural robotics, establishing him as an emerging leader in smart farming technologies.
Research Focus
Key Achievements
Top Papers
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- 2