用直方图匹配提升葡萄病害识别在复杂光照下的稳定性
Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection
- 将直方图匹配分两阶段应用：预处理归一化与数据增强
- 在1469张图像上训练，田间图像识别准确率显著提升
- 适合光照变化大的野外植物病害检测任务
光照变化是制约基于深度学习的田间植物病害检测鲁棒性的主要因素。本研究评估了直方图匹配（HM）技术在葡萄植株病害分类中的应用，用于区分健康叶片、霜霉病和红蜘蛛损伤。提出一种双阶段整合策略：(i) 作为预处理进行图像强度分布归一化，(ii) 作为数据增强手段引入可控的训练变异性。利用1,469张RGB图像（包含同质叶面样本与异质冠层样本）训练ResNet-18模型，实验表明该组合显著提升了对真实田间冠层图像的鲁棒性。虽然叶面样本增益有限，但冠层子集表现明显改善，说明在归一化与直方图多样化之间取得平衡，能有效缓解由不可控光照引起的域偏移。
原文摘要 · Abstract (English)
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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