# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 3 Results
### Model Predictive Analytics
In most categories, the lightweight GoogLeNet maintained comparable prediction accuracy to the original GoogLeNet, and performed particularly well when excluding the wrong categories. For example, in both the Crassula obliqua 'Gollum' and Sedum burrito categories, the lightweight GoogLeNet's prediction probability of 0.0 accurately excluded these categories, showing its excellent exclusion of non-target categories. This result is close to the GoogLeNet model, but the lightweight GoogLeNet requires significantly fewer computational resources, demonstrating its efficiency advantage. In contrast, AlexNet and VGG16 did not perform as well as lightweight GoogLeNet in predicting certain categories. e.g., in the Haworthia truncate category, lightweight GoogLeNet exhibited a prediction close to 0.0, indicating that it can accurately exclude this category, whereas the prediction probability of 8.83e\({}^{-05}\) for VGG16 shows a certain degree of categorization uncertainty. This indicates that lightweight GoogLeNet reduces the possibility of misclassification while maintaining high accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **AlexNet** & **VGG16** & **GoogLeNet** & **Lightweight GoogLeNet** \\ \hline _Senecio hauorthii_ & \(3.05\times 10^{-8}\) & \(3.65\times 10^{-5}\) & \(3.87\times 10^{-10}\) & 0.0 \\ _Crassula perforata_ & \(8.29\times 10^{-7}\) & \(1.66\times 10^{-4}\) & \(2.68\times 10^{-9}\) & \(2.38\times 10^{-7}\) \\ _Crassula obliqua ‘[PERSON]_ & \(1.34\times 10^{-11}\) & \(6.56\times 10^{-6}\) & \(3.45\times 10^{-13}\) & 0.0 \\ _Echeveria chiluhaunaensis_ & \(1.15\times 10^{-6}\) & \(2.74\times 10^{-4}\) & \(7.4\times 10^{-8}\) & \(4.26\times 10^{-5}\) \\ _Echeveria agavoides ‘[PERSON]_ & \(5.29\times 10^{-10}\) & \(9.03\times 10^{-6}\) & \(4.6\times 10^{-11}\) & 0.0 \\ _Graptoveria ‘Opalina’_ & 1.0 & 0.999 & 1.0 & 1.0 \\ _Haworthia cooperi var.truncata_ & \(2.98\times 10^{-6}\) & \(3.16\times 10^{-4}\) & \(1.78\times 10^{-8}\) & \(1.19\times 10^{-7}\) \\ _Haworthia truncata_ & \(9.38\times 10^{-9}\) & \(8.83\times 10^{-5}\) & \(3.77\times 10^{-8}\) & \(5.96\times 10^{-8}\) \\ _Sedum burrito_ & \(1.46\times 10^{-9}\) & \(1.16\times 10^{-5}\) & \(1.77\times 10^{-12}\) & 0.0 \\ _Sedum rubrotinctum_ & \(5.27\times 10^{-9}\) & \(9.15\times 10^{-6}\) & \(1.29\times 10^{-11}\) & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different networks for succulent plant classification. Overall, the lightweight GoogLeNet not only maintains similar prediction performance to the original GoogLeNet in several categories, especially in the categories of Senecio haworthii and Echeveria agavoides 'Ebony', but its classification exclusion effect is much better than that of AlexNet and VGG16. Meanwhile, it significantly reduces computational costs, which makes the model more practical in real applications. Through this experimental comparison, lightweight GoogLeNet demonstrates the ability to optimize computational resources while maintaining model accuracy, making it suitable for deployment in resource-constrained environments. Therefore, lightweight GoogLeNet not only retains the high-precision performance of GoogLeNet but also highlights its potential value in real-world applications through higher efficiency and accurate category exclusion. This advantage provides strong support for deployment in large-scale image classification tasks.