An Attention-Enhanced Lightweight CNN with Cross-Domain Robustness for Edge-Based Plant Disease Identification

The main contributions of this paper are threefold. First, this
work integrates CBAM into a MobileNetV3-Large backbone
to improve attention to plant disease lesions while keeping the
model suitable for edge-oriented deployment. The resulting
architecture contains only 4.36 million trainable parameters,
which makes it more practical for low-resource agricultural
monitoring than heavier CNN backbones. Second, this work
uses Grad-CAM to examine whether the model is learning
visually meaningful disease regions. The attention maps show
that the classifier focuses on lesions and infected tissue rather
than relying only on background texture or leaf placement.
Third, this work evaluates the same trained checkpoint on
both PlantVillage and PlantDoc, reporting both controlled
laboratory accuracy and the cross-domain failure that appears
when the model is tested on field-style images.