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Background <p>Early detection of grapevine downy mildew (GDM) using unmanned aerial vehicle (UAV) imagery remains highly challenging due to limited onboard computing power, natural light overexposure, limited imaging resolution that obscures subtle early lesions, pronounced symptom differences between leaf surfaces, and severe canopy occlusion.</p> Models <p>To address these challenges, a lightweight GDM detection network (GDM-Net) for UAV-based applications is proposed. A CLAHE–Unsharp Masking–Gamma Correction (CUG) image enhancement framework is proposed to achieve synergistic multi-dimensional feature enhancement by expanding the regional dynamic range, emphasizing high-frequency lesion details, and redistributing image brightness. This effectively alleviates the issue of weak and difficult-to-recognize early lesion features. To enhance feature extraction with high efficiency and accuracy, the YOLOv11n backbone integrates an Adaptive Instance-aware Feature Interaction (AIFI) mechanism inspired by RT-DETR, achieving sparse and efficient feature interaction with minimal computational cost. Furthermore, an Adaptive Dual-Path Fusion Attention (ADFA) module is designed in the neck architecture to handle dense canopy occlusion. It performs multiscale convolutional feature extraction, cross-attention interaction, and adaptive dynamic fusion between feature pathways, thereby improving the discriminative capacity under occlusion and clustering conditions.</p> Results <p>Experimental results demonstrate that the CUG image enhancement framework significantly improves early lesion visibility and feature separability under complex illumination conditions. GDM-Net achieves accuracy and lightweight ability, demonstrating superior onboard deployment feasibility compared with other mainstream models. It achieves a 5.6% improvement in mAP@50 and a 6.5% improvement in mAP@50:95, while requiring only 7.5 GFLOPs of computational cost, addressing a key bottleneck in UAV-based GDM detection.</p> Conclusions <p>GDM-Net provides an efficient and practical solution for UAV-based crop disease monitoring by integrating image information enhancement and improved lightweight detection. Its balance of computational efficiency and detection precision highlights its potential for broader applications in precision agriculture and intelligent crop health management systems.</p>

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Lightweight detection of grapevine downy mildew using UAV based on image information enhancement and adaptive dual-path fusion attention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月29日

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  • Chunyang Li,
  • Yan Teng,
  • Chun Li,
  • Zhengwei Huang,
  • Ling Jiang

摘要

Background

Early detection of grapevine downy mildew (GDM) using unmanned aerial vehicle (UAV) imagery remains highly challenging due to limited onboard computing power, natural light overexposure, limited imaging resolution that obscures subtle early lesions, pronounced symptom differences between leaf surfaces, and severe canopy occlusion.

Models

To address these challenges, a lightweight GDM detection network (GDM-Net) for UAV-based applications is proposed. A CLAHE–Unsharp Masking–Gamma Correction (CUG) image enhancement framework is proposed to achieve synergistic multi-dimensional feature enhancement by expanding the regional dynamic range, emphasizing high-frequency lesion details, and redistributing image brightness. This effectively alleviates the issue of weak and difficult-to-recognize early lesion features. To enhance feature extraction with high efficiency and accuracy, the YOLOv11n backbone integrates an Adaptive Instance-aware Feature Interaction (AIFI) mechanism inspired by RT-DETR, achieving sparse and efficient feature interaction with minimal computational cost. Furthermore, an Adaptive Dual-Path Fusion Attention (ADFA) module is designed in the neck architecture to handle dense canopy occlusion. It performs multiscale convolutional feature extraction, cross-attention interaction, and adaptive dynamic fusion between feature pathways, thereby improving the discriminative capacity under occlusion and clustering conditions.

Results

Experimental results demonstrate that the CUG image enhancement framework significantly improves early lesion visibility and feature separability under complex illumination conditions. GDM-Net achieves accuracy and lightweight ability, demonstrating superior onboard deployment feasibility compared with other mainstream models. It achieves a 5.6% improvement in mAP@50 and a 6.5% improvement in mAP@50:95, while requiring only 7.5 GFLOPs of computational cost, addressing a key bottleneck in UAV-based GDM detection.

Conclusions

GDM-Net provides an efficient and practical solution for UAV-based crop disease monitoring by integrating image information enhancement and improved lightweight detection. Its balance of computational efficiency and detection precision highlights its potential for broader applications in precision agriculture and intelligent crop health management systems.