Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection

• URL: http://arxiv.org/abs/2604.19510v1
• Date: Tue, 21 Apr 2026 14:27:21 GMT
• Title: Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection
• Abstract summary: Histogram Matching is a technique that transforms the pixel intensity distribution of an image to match a reference profile.<n>We propose a dual-stage integration of HM: (i) as a preprocessing step for normalization, and (ii) as a data augmentation technique to introduce controlled training variability.
• Score: 3.9714494364463526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogram Matching (HM), a technique that transforms the pixel intensity distribution of an image to match a reference profile, to mitigate this in grapevine classification, distinguishing among healthy leaves, downy mildew, and spider mite damage. We propose a dual-stage integration of HM: (i) as a preprocessing step for normalization, and (ii) as a data augmentation technique to introduce controlled training variability. Experiments using 1,469 RGB images (comprising homogeneous leaf-focused and heterogeneous canopy samples) to train ResNet-18 models demonstrate that this combination significantly enhances robustness on real-world canopy images. While leaf-focused samples showed marginal gains, the canopy subset improved markedly, indicating that balancing normalization with histogram-based diversification effectively bridges the domain gap caused by uncontrolled lighting.

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