Leakage-Controlled Tomato Leaf Disease Classification Using Attention-Guided CNN and GCN Feature Fusion

Accurate tomato leaf disease recognition can be
overstated when visually repeated images occur across data
partitions or evaluation is restricted to familiar sources. This
study presents a leakage-controlled classification framework that
combines EfficientNetV2-S, the Convolutional Block Attention
Module (CBAM), an inductive Graph Convolutional Network
(GCN), and feature-level fusion. Starting from 31,450 cleaned
images representing 11 tomato leaf classes, a perceptual-hash
audit identified 1,237 repeated-image groups involving 2,539
images. After removing 24 cross-class conflicts and one corrupted
image, 31,425 images were partitioned group-wise into 21,990
training, 4,718 validation, and 4,717 held-out test samples, with
no verified SHA-256, perceptual-hash, or source-group overlap
across the partitions. The attention-enhanced network produced
1,280-dimensional image embeddings, while a cosine-neighbor
graph with k = 5 and an inductive graph network generated 128-
dimensional relational features.