Abstract: Efficient and accurate automated diagnosis of plant diseases remains a challenge for deployment on resource-constrained edge devices. While hybrid vision transformers like GCViT balance accuracy and efficiency, they often lose critical high-frequency details such as fine lesion textures and leaf margins that are essential for fine-grained disease classification. To address this gap, we propose the Enhanced High-Frequencies Global Context Visual Transformer (EHF-GCViT), a novel hybrid architecture designed to explicitly enhance high-frequency feature retention within a lightweight framework. The core innovations of EHF-GCViT include: first, a customized, lightweight convolutional refinement block based on depthwise separable operations that acts as a learnable pre-processor to preserve discriminative spatial details before tokenization; second, a gated convolutional block that replaces the final transformer stage, reducing the model memory footprint from 46.36 MB to 34.48 MB; and third, an adaptive normalization strategy to stabilize the training of the integrated heterogeneous layers. Extensive experiments on the PlantVillage tomato disease dataset demonstrate that EHF-GCViT achieves superior performance, surpassing the baseline GCViT, standard Vision Transformers (ViT), and CNN benchmarks (e.g., ResNet) in accuracy, precision, recall, and F1-score. These results validate that explicitly modeling high-frequency features within a hybrid transformer design provides a more memory-efficient and accurate backbone for practical plant disease detection systems targeting edge deployment.

Mohamed Jawher Bahrouni, Faouzi Benzarti, Mohamed Touati and Sadok Ben Yahia. “Enhanced Mobile GC Vit Architecture for Efficient Image Classification with Application to Plant Disease Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01612108

@article{Bahrouni2025,
title = {Enhanced Mobile GC Vit Architecture for Efficient Image Classification with Application to Plant Disease Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01612108},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01612108},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Mohamed Jawher Bahrouni and Faouzi Benzarti and Mohamed Touati and Sadok Ben Yahia}
}