DS_FusionNet: Dynamic Dual-Stream Fusion with Bidirectional Knowledge Distillation for Plant Disease Recognition
On the map: Knowledge distillation → Detection and dense prediction distillation
Abstract (arXiv)
Given the severe challenges confronting the global growth security of economic crops, precise identification and prevention of plant diseases has emerged as a critical issue in artificial intelligence-enabled agricultural technology. To address the technical challenges in plant disease recognition, including small-sample learning, leaf occlusion, illumination variations, and high inter-class similarity, this study innovatively proposes a Dynamic Dual-Stream Fusion Network (DS_FusionNet). The network integrates a dual-backbone architecture, deformable dynamic fusion modules, and bidirectional knowledge distillation strategy, significantly enhancing recognition accuracy. Experimental results demonstrate that DS_FusionNet achieves classification accuracies exceeding 90% using only 10% of the PlantDisease and CIFAR-10 datasets, while maintaining 85% accuracy on the complex PlantWild dataset, exhibiting exceptional generalization capabilities. This research not only provides novel technical insights for fine-grained image classification but also establishes a robust foundation for precise identification and management of agricultural diseases.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation. Builds on 6 of its 14 references that are in the map. First posted on arXiv 29 Apr 2025.
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6 of 14 references.
References · 6
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2022A ConvNet for the 2020scited by 3,579
- 2021Video Swin Transformercited by 1,091
- 2017Remote Sensing Image Fusion Based on Two-stream Fusion Networkcited by 25
- 2022Improving the Intra-class Long-tail in 3D Detection via Rare Example Miningcited by 19
- 2023BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphscited by 7
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- 2026AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification
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- 2026Cross-Architectural Mixture-of-Experts with Adaptive Soft Routing for Plant Leaf Disease Classification
- 2026Cross-Architecture Knowledge Distillation from a Vision Foundation Model to a Lightweight Visual State Space Model for Tea Leaf Disease Classification
- 2026Cross-Dataset Generalization of Bangladeshi Rice Leaf Disease Classifiers: Benchmark, Diagnosis, and Mitigation
- 2026TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis
- 2025Multi-objective hybrid knowledge distillation for efficient deep learning in smart agriculture
- 2026AdapterMoE: A Two-Stage Hard-Routing Mixture-of-Experts Architecture for Multi-Crop Disease Recognition with Calibrated Rejection and Incremental Learning