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About Me

👨🏼‍🌾Hi! This is Xiangyu, a Ph.D. candidate in iGE-Lab/Prof. LIU at Zhejiang University (ZJU). ⚡My research focuses on UAV-based remote sensing, image segmentation and super-resolution, especially in Agro&Ecological scenes. 🎓I anticipate to graduate by the end of this year and is currently seeking academic positions.

🍀I have been working on the Weakly Supervised Weed Segmentation using the class-activation-map from severity-grading model on UAV images during 2024. I constructed a varianve-attention enhanced Diffusion Model for effective field UAV image super-resolution during 2023, which is promised to bridge UAV and satellite🛰️ data to achieve large-area remote sensing image refinement. 🐣In 2022, I designed a direct geo-locating and trait-mapping workflow for UAV image sequence, to achieve the real-time monitorning of crop field. 🔑 I am now working on the Paddy field abnormal region (pest/disease stress) segmentation and preparing my doctoral thesis.

💡My future interests is to explore learning-based vision systems through UAV and remote sensing data, toward intelligent environmental perception and understanding of Agro-Ecological systems🌳.


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Articles

Lu X, Zhang J, Yang R, et al. Effective variance attention-enhanced diffusion model for crop field aerial image super resolution. ISPRS Journal of Photogrammetry and Remote Sensing. 2024; 218:50-68. https://doi.org/10.1016/j.isprsjprs.2024.08.017

Lu X, Zhou J, Yang R, et al. Automated Rice Phenology Stage Mapping Using UAV Images and Deep Learning. Drones. 2023; 7(2):83. https://doi.org/10.3390/drones7020083

Lu X, Yang R, Zhou J, et al. A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest. Journal of King Saud University - Computer and Information Sciences. 2022;34(5):1755-1767. https://doi.org/10.1016/j.jksuci.2022.03.006