Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Pascual, Ruben Hernández, Inés Gutiérrez, Salvador Tardaguila, Javier Melo-Pinto, Pedro Paternain, Daniel Galar, Mikel |
| author_facet | Pascual, Ruben Hernández, Inés Gutiérrez, Salvador Tardaguila, Javier Melo-Pinto, Pedro Paternain, Daniel Galar, Mikel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19510 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection Pascual, Ruben Hernández, Inés Gutiérrez, Salvador Tardaguila, Javier Melo-Pinto, Pedro Paternain, Daniel Galar, Mikel Computer Vision and Pattern Recognition 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. |
| title | Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.19510 |