Recent achievements of the team - An end-to-end object detection framework for real-world plant leaf disease diagnosis
Our team published the paper “PDDNet: An End-to-End Object Detection Framework for Real-World Plant Leaf Disease Diagnosis” in Expert Systems with Applications (IF: 7.5, QSCI Zone 1). The School of Computer Engineering and Science at Shanghai University is the first institution listed.
Plant leaf disease detection is an important task in smart agriculture, precision plant protection, and crop health management. However, in real-world agricultural scenarios, leaf lesions are often affected by complex natural backgrounds, multi-scale disease regions, lighting variations, and subtle visual differences between different disease categories. As a result, existing detection methods still face challenges in localization accuracy, classification robustness, and cross-scene generalization. To address this problem, this paper proposes PDDNet, an end-to-end plant leaf disease detection framework that integrates local lesion details with global contextual information through a cascaded encoder-decoder structure, thereby improving disease detection performance in real-world scenarios.
Specifically, we propose an Enhanced Attention-based Multi-scale Aggregation (EAMA) module that strengthens the feature representation capability for lesion regions at different scales through collaborative modeling of spatial attention and channel attention. Meanwhile, a Prior-guided Self-Attention (PGSA) mechanism is introduced to incorporate position priors and IoU geometric relationships into attention computation, enabling the model to focus more effectively on lesion boundaries and morphological structures. Furthermore, this paper designs a Multi-task Feature Decoupling Module (MFDM), which separates classification features from localization features using task-specific dynamic masks, alleviating conflicts between classification and regression tasks. Experimental results on real-world datasets such as PlantDoc and Tomato Leaf Disease show that PDDNet achieves favorable detection performance in complex backgrounds, multi-scale lesion detection, and fine-grained category recognition tasks, providing reliable technical support for automated disease diagnosis in precision agriculture.
Essay: PDDNet: An End-to-End Object Detection Framework for Real-World Plant Leaf Disease Diagnosis
