Dual-stage histogram matching as normalization and augmentation improves ResNet-18 robustness for grapevine disease detection, with marked gains on heterogeneous canopy images.
Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa)
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Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection
Dual-stage histogram matching as normalization and augmentation improves ResNet-18 robustness for grapevine disease detection, with marked gains on heterogeneous canopy images.