Dual-stage histogram matching as normalization and augmentation improves ResNet-18 robustness for grapevine disease detection, with marked gains on heterogeneous canopy images.
From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.