TinyResViT: A lightweight hybrid deep learning model for on-device corn leaf disease detection

Abstract

The increasing prevalence of corn leaf diseases poses a significant threat to global food security, necessitating efficient and accurate detection methods. To address this challenge, we introduce TinyResViT, a lightweight yet efficient hybrid deep learning model designed by combining Residual Network (ResNet) and Vision Transformer (ViT) for leaf disease detection. This combination leverages the strengths of ResNet in extracting local features and ViT in capturing global interactions among features. In addition, a novel downsampling block connecting ResNet and ViT is proposed to eliminate redundant model weights. The evaluation results on the PlantVillage and Bangladeshi Crops Disease datasets show TinyResViT’s superior performance, achieving F1-scores of 97.92% and 99.11%, respectively. The model also maintains a high processing speed of 83.19 Frames Per Second (FPS) and a low computational cost of 1.59 Giga Floating Point Operations (GFLOPs), outperforming existing deep neural networks and state-of-the-art approaches.