# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
## 4 Discussion  
In this study, we delved into the characteristics and architectures of five lightweight models and compared their performance in terms of accuracies, precisions, recalls, and F1 scores on the sunflower disease image dataset. The evaluated models included SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficientNetV2-Small. Initially, these models achieved sunflower disease identification accuracies of 84.08%, 79.31%, 88.59%, 84.08%, and 90.19%, respectively, before incorporating transfer learning. To address the challenge of a relatively small dataset and enhance the accuracy of each model, we employed pre-training models and the transfer learning method. Consequently, the accuracies of these models improved by 14.2%, 20%, 7.2%, 15.2%, and 10%, reaching 96.02%, 95.23%, 94.96%, 96.82%, and 99.20%. Upon comparing the performances of the five models, we found that EfficientNetV2-Small outperformed the other four models in terms of precision, recall, F1 score, and accuracy. [PERSON] and [PERSON] [19] designed a module to extract multi-frequency multi-scale features and proposed lightweight dual-fusion attention and multi-branching structure to identify sunflower disease. This method named TeenyNet obtained the highest accuracy of 98.94%. SunNet [30] is a deep learning approach for detecting sunflower diseases. The study investigated various disease classification techniques on 1428 sunflower leaf images and found that the best-performing classifier overall was a ResNet50 classifier, which achieved an average accuracy of 97.88%. [PERSON] et al. [31] compared the performance of five models in the identification of sunflower diseases, such as AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNetB3, and finally found that Efficient \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Models** & **Average** & **Average** & **Average** & **Average** \\  & **Precision** & **Recall** & **F1-Score** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{Before transfer learning} & SqueezeNet & 0.846 & 0.847 & 0.846 & 84.08 \\  & ShuffleNetV2 & 0.793 & 0.797 & 0.795 & 79.31 \\  & MnasNet-A1 & 0.890 & 0.889 & 0.889 & 88.59 \\  & MobileNetV3-Small & 0.840 & 0.846 & 0.841 & 84.08 \\  & EfficientNetV2-Small & 0.904 & 0.903 & 0.903 & 90.19 \\ \hline \multirow{4}{*}{After transfer learning} & SqueezeNet & 0.962 & 0.961 & 0.961 & 96.02 \\  & ShuffleNetV2 & 0.955 & 0.954 & 0.954 & 95.23 \\ \cline{1-1}  & MnasNet-A1 & 0.953 & 0.951 & 0.952 & 94.96 \\ \cline{1-1}  & MobileNetV3-Small & 0.969 & 0.970 & 0.969 & 96.82 \\ \cline{1-1}  & EfficientNetV2-Small & 0.992 & 0.992 & 0.992 & 99.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The average precision, recall, F1 score, and accuracy of four classes of image identification in the test dataset (including 377 images) of five models before and after transfer learning. NetB3 had the highest accuracy of 97.9%. The lightweight model DFN-PSAN [32] achieved an accuracy of 94.47% on the sunflower dataset. The dataset sizes used in these methods are shown in Table 9.  
Compared with TeenyNet [19], VGG19+CNN [29], SunNet [30], EfficientNetB3 [31], and DFN-PSAN [32], which are also used for detecting sunflower disease, EfficientNetV2-Small (in our study) also achieved the best accuracy. The comparison graph is shown in Figure 11.  
From Figure 11, we found the accuracy of EfficientNetV2-Small reached the highest accuracy of 99.2% in these models. There were several reasons why EfficientNetV2-Small achieved a relatively high recognition accuracy. First, EfficientNetV2-Small employed the method of compound scaling. By uniformly adjusting the model's depth, width, and image resolution, it achieved the optimal balance between performance and efficiency. This scaling method allowed the model to leverage the benefits of increased capacity while maintaining computational efficiency, which was crucial for lightweight models designed for real-time applications. Second, EfficientNetV2-Small incorporated variants of MobileNetV3-Small and Fused-MBConv.