Lightweight global-local dual-branch fusion for representative multicrop disease recognition

Abstract

Multi-crop disease recognition becomes difficult when visually similar lesion patterns must be identified under a tight parameter budget. This paper reports a compact conference-scale study for the computer-vision and machine-learning track of MLES 2026. A representative 14-class subset covering tomato, cucumber, grape, and apple was constructed from 13,205 images, including 10,272 training images and 2,933 held-out evaluation images. The proposed network couples a lightweight global branch, implemented by a shallow CNN stem followed by a Transformer encoder, with a MobileNetV3- Small local branch for texture-sensitive feature extraction. A learned gating head projects and adaptively fuses global and local evidence before classification. On a single RTX 3060 GPU, the model achieved 99.35% Top-1 accuracy and 100.00% Top-5 accuracy, with macro precision, recall, F1-score, and specificity of 99.36%, 99.39%, 99.37%, and 99.95%, respectively. The model uses only 2.17M parameters, indicating that accurate and deployable visual recognition is possible with a compact dual-branch design. To address reviewer concerns on robustness and component attribution, the revised manuscript additionally reports five-fold cross-validation statistics, single-branch baselines, augmentation ablations, and a freezing-strategy study.