DECL: Disease Evidence Consistency Learning for Robust Plant Disease Classification

Plant disease classification models can achieve high
predictive accuracy while remaining sensitive to appearance variations and relying on inconsistent visual cues. We propose Disease
Evidence Consistency Learning (DECL), a transformation-aware
framework that encourages stable disease representations and
spatial evidence across disease-preserving views of the same
image. DECL employs a pretrained ResNet18 backbone with
a learnable disease-evidence module that generates spatial evidence maps and constructs evidence-weighted feature representations. Training jointly optimizes classification loss, feature level consistency, and transformation-aware evidence consistency. Specifically, cosine consistency encourages invariant disease
representations, while spatial evidence maps are geometrically
realigned before consistency regularization, promoting invariance
to photometric changes and equivariance to geometric transformations. This enables the model to learn stable task-relevant
evidence without requiring lesion-level annotations. Experiments
on bean leaf disease classification show that DECL achieves an
accuracy of 99.04%, providing an absolute improvement of 3.85
percentage points over standard training. DECL also produces
well-calibrated predictions, achieving an Expected Calibration
Error (ECE) of 0.75% and a Brier score of 0.0185. These results
demonstrate that jointly enforcing representation and disease evidence consistency can improve both predictive performance
and reliability in plant disease recognition.