# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 5 Conclusions  
This study primarily focused on classifying tomato leaf diseases by proposing a custom fusion model and comparing its performance with that of existing mainstream image classification models. To extract features from tomato disease images more effectively, we first apply a self-attention-based Transformer structure to tomato leaf disease image classification and detection and propose the Eff-Swin model, which integrates the EfficientNetv2 and Swin Transformer structures. To improve the effectiveness of CNN feature extraction on tomato disease image features and avoid overfitting caused by model fusion, we replaced the SE module in EfficientNetv2 with a CA module in the Eff-Swin model. Additionally, we redesigned the network architecture of EfficientNetv2 as the CNN backbone of Eff-Swin while employing the Swin-Transformer-based structure to construct the Transformer backbone. The experimental results demonstrate that the Eff-Swin model achieves a remarkable accuracy of 99.70% on the tomato leaf disease dataset. We also conducted comprehensive experiments using other state-of-the-art methods. The results indicate that for the tomato disease dataset, the Eff-Swin model outperforms the best-performing CNN model in the experiments by a significant margin of 1.16%, the best-performing Transformer model \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Dataset Size** & **Number of Class** & **Used Architecture** & \begin{tabular}{c} **Best Model** \\ **(Accuracy)** \\ \end{tabular} \\ \hline [PERSON] et al. [19] & 2018 & 5500 & 9 & AlexNet, GoogLeNet, & ResNet with SGD \\  & & & & ResNet & (96.51\%) \\ [PERSON] et al. [20] & 2019 & 17,500 & 10 & VGG-16, & Custom CNN (91.2\%) \\ Elhassouny and & 2019 & 7176 & 10 & MobileNet & MobileNet \\ [PERSON] [21] & 2020 & \begin{tabular}{c} Laboratory 2364, \\ field-based \\ 15,216 \\ 2020 \\ \end{tabular} & 4,6 & \
\begin{tabular}{c} VGG-16, VGG-19 ResNet, \\ InceptionV3 \\ 12,206 \\ \end{tabular} & InceptionV3 & InceptionV3 (99.60\%) \\ [PERSON] et al. [23] & 2020 & 12,206 & 6 & ResNet-50 & ResNet-50 (97.00\%) \\ [PERSON] et al. [24] & 2020 & 2000 & 5 & DT, RT & RT (94.00\%) \\ [PERSON] et al. [25] & 2020 & 8616 & 5 & B-ARNet & B-ARNet (98.00\%) \\ [PERSON] et al. [26] & 2020 & 16,578 & 10 & Modified-Xception & Modified-Xception & (99.55\%) \\ [PERSON] et al. [27] & 2021 & 4585 & 10 & ResNet-50, SE-ResNet-50 &
\begin{tabular}{c} SE-ResNet-50 \\ (96.81\%) \\ \end{tabular} \\ [PERSON] et al. [28] & 2022 & 22,390 & 10 & AlexNet & AlexNet \\ H S and & & & & (98.00\%) \\ [PERSON] H [17] & 2022 & 735 & 7 & Fuzzy-SVM, CNN, R-CNN & R-CNN \\  & & & & (96.73\%) \\ [PERSON] et al. [29] & 2023 & 8190 & 5 & ResNet-101, & DIMCNET (94.44\%) \\  & & & & DIMCNET & \\ [PERSON] et al. [18] & 2024 & 18,160 & 10 & VGG19, Resnet101, Swin & DVT \\  & & & Transformer, DVT & (99.45\%) \\ Our proposed & — & 17,043 & 13 & Eff-Swin & Eff-Swin model (99.70\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result comparisons with related studies. (2021) by 3.68%, and the best-performing fusion model by 0.8%. These results underscore the exceptional classification performance of the Eff-Swin model in tomato leaf disease image classification and recognition tasks. Furthermore, the model evaluation reveals that the incorporation of appropriate attention mechanisms significantly enhances the robustness of the Eff-Swin model. Among the models employed in this study, the improved EfficientNetv2 model achieves a high accuracy of 99.21%, notably outperforming other studies that have attempted the same task. In conclusion, the Eff-Swin model represents a pioneering attempt to combine EfficientNetV2 with the Swin Transformer and apply it to the challenging task of tomato leaf disease recognition. The model's outstanding feature extraction capabilities and extensive potential for application in such tasks have been clearly demonstrated.