Congratulations to Ma Weiyi on her successful graduation!

Ma Weiyi graduated from Shandong University of Technology and began her professional master’s degree in the School of Computer Engineering and Science at Shanghai University in 2023. After joining the research group, she studied computer vision under the guidance of Professor Yang Fenglei, Professor Han Yuexing, and Professor Chen Qiaochuan. With their careful guidance, she completed the following research:

1. To address the weak multi-scale feature representation and continuous semantic attenuation in hierarchical fusion during field plant leaf disease detection, the HRAGNet detection algorithm is proposed. An interactive enhancement module is designed to achieve bidirectional recursive enhancement of cross-level features, a cross-scale semantic alignment module is used to correct feature spatial misalignment, and a global context aggregation and fusion module integrates multi-scale global information, effectively improving the feature discrimination ability for disease spots of different sizes in complex scenes.

2. To address the limitations of HRAGNet in the decoding stage, including the lack of spatial priors for disease spots and conflicts caused by feature coupling between classification and regression tasks, an improved algorithm, HRAGNet+, is proposed, constructing a geometry-task collaborative enhanced decoder. A geometric prior enhancement attention module is designed to explicitly model the spatial distribution relationships among disease spots. A task feature decoupling and fusion module is also built to adaptively separate classification-specific and regression-specific features, alleviating interference in multi-task optimization and significantly improving the localization and classification accuracy of tiny and dense disease spots.

3. Multiple public agricultural vision datasets, including PlantDoc, TLD, and FD, are selected for comparative experiments and ablation studies. Through multiple quantitative metrics, visualized samples, and robustness tests under extreme lighting conditions, the comprehensive performance of the two models is verified, fully demonstrating the effectiveness and generalization ability of multi-scale feature enhancement and geometry-task collaborative optimization mechanisms in leaf disease detection tasks.

4. After graduation, Ma Weiyi joined ByteDance in Beijing and will work in product operations. During her postgraduate studies at Shanghai University, Ma Weiyi studied diligently, kept improving, and continuously enhanced her professional and research capabilities. She was fortunate to work alongside many excellent mentors and friends and gained a great deal along the way. We hope that in her future journey she will always keep her ideals in mind, stay grounded, fear no hardship, and move forward bravely.

Essay: Research on Plant Leaf Disease Detection Methods Based on Multi-Scale Feature Enhancement and Fusion

Code: https://github.com/han-yuexing/2026-thesis-mwy-code