# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 3 Results
### Comparative Analysis of Data Enhancement Models  
In order to verify the robustness of the improved model to the size of the dataset, we extended it by applying various data enhancement strategies to the original image dataset. Specific enhancement methods include: random horizontal flipping to simulate changes in left and right viewpoints, random rotation to generate image views with different angles, color dithering, which entails the random perturbation of brightness, contrast, saturation, and hue to simulate complex lighting conditions, and random cropping and scaling, which is an adjustment of image field of view ranges and enhances the learning ability of local features. Through these enhancement operations, five diverse enhanced images were generated for each original image. The enhanced data significantly increased the diversity in terms of spatial transformations, color distributions, and image contents, successfully simulating complex real-world application scenarios. These enhancement strategies not only effectively improved the generalization ability of the deep learning model, but also enhanced its robustness to image transformations. In addition, data augmentation effectively mitigates the overfitting risk of the model by expanding the diversity of the data under limited sample conditions, and provides the model with more comprehensive and diverse training samples. This enables the model to perform more stably and accurately under complex conditions. Eventually, the number of images in the original dataset was expanded from 691 to 3455 succulent images, providing rich data support for subsequent experiments. In order to verify the robustness of the improved model to the dataset size, we conducted experiments on the original dataset of 691 images and the extended dataset of 3455 images after data enhancement (as shown in Figure 7). In the original dataset, the training set was 626 images and the test set was 65 images, and the final validation accuracy of the model reached 98.5%, with a training loss as low as 0.001, which demonstrates the efficient learning ability and excellent performance of the improved model under small dataset conditions. In the extended dataset after data enhancement, the training set contained 3180 images and the test set 342, and the validation accuracy was slightly improved to 99.4%, while the training loss was kept at 0.001 (as shown in Table 3). This comparison experiment verifies the efficient learning and stability of the improved model in a small dataset environment, and the effect of data augmentation was shown to be minimal due to saturation, although it did improve the accuracy. The robustness and effectiveness of the model have been fully demonstrated under the conditions of a small dataset with strong adaptive ability, especially in application scenarios where data scarcity is an issue. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Number of Images** & **Training Set** & **Test Set** & **Train\_Loss** & **Val\_Accuracy (\%)** \\ \hline \hline \multirow{2}{*}{Succulent dataset} & 691 & 626 & 65 & 0.001 & 98.5 \\  & 3455 & 3180 & 342 & 0.001 & 99.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the original dataset and the enhanced dataset. Figure 7: Data Enhancement Approach. By comparing the experimental results on datasets of different sizes, it can be seen that the improved model is able to adapt effectively to both small and large datasets, and the validation accuracy is always maintained at a high level. Although the data enhancement technique further optimizes the generalization ability of the model by extending the data diversity, the experimental results on small-scale datasets show that the model is still able to achieve high performance stably under data-constrained conditions. This result fully demonstrates that the model is insensitive to the size of the dataset and also has good robustness under a limited sample size.