# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 1 Introduction
Due to the difficulty involved in identifying them with the naked eye, traditional manual classification methods are inefficient and rely on expert knowledge, making it difficult to meet large-scale automation needs. Therefore, the core challenge in the task of classifying succulents is how to improve the generalization ability of the model with a limited dataset. To solve the above problems, researchers have proposed various effective strategies in recent years, including data augmentation, dropout methods, and label regularization. Data augmentation methods help models learn more robust features by expanding sample diversity and are a common means of alleviating overfitting. Methods such as CutMix [17] and RandAugment [18] significantly improve the adaptability of models in complex image tasks through image cropping, splicing, and transformation. Dropout-like methods such as MaxDropout [19] and DropBlock [20] reduce the model's dependence on specific weights and alleviate the overfitting of deep networks. Label regularization methods such as Label Smoothing [21] and JoCoR [22] have also achieved remarkable results in dealing with noisy labels and improving model stability. The above research provides an important technical reference for the task of image classification of succulents. However, in the task of classifying succulents, the scarcity of data, small differences between classes, and large differences within classes significantly exacerbate the problem of overfitting the model. To alleviate this problem, researchers have proposed various effective strategies, among which data augmentation techniques have achieved remarkable results as an important means to improve the generalization ability of models. [PERSON] et al. [23] introduced advanced enhancement methods such as Cutout and Mixup, combined with a variety of image transformation strategies, which improved the model accuracy from 67.35% to 78.68%, significantly improving the robustness of the model in complex scenarios. Similarly, [PERSON] et al. [24] used data augmentation methods such as random flipping, rotation, and color jittering, combined with transfer learning strategies, to improve the model's test set accuracy from 59.48% to 96.90%, effectively alleviating the overfitting of the model. In addition, in recent years, researchers have gradually introduced attention mechanisms to improve the model's ability to focus on key features, thereby exhibiting excellent performance in fine-grained classification tasks [25; 26]. While emphasizing significant regions of an image, the attention mechanism can effectively suppress redundant features, further improving the model's classification accuracy in complex images. To further address the problem of data scarcity, researchers have begun to explore few-shot learning methods to enable effective training of models on small datasets. The few-shot learning model combined with an attention mechanism aggregates multi-level fine-grained features through a multi-branch structure, achieving more accurate feature representation and classification performance, especially in the task of succulent plant classification with limited data resources [27; 28]. The above studies show that combining methods such as data augmentation and attention mechanisms provides an effective way to solve the overfitting problem in succulent plant classification and improve the generalization performance of the model, opening up new research directions for the application of deep learning in the analysis of complex images. In summary, this paper proposes a multi-succulent plant classification algorithm framework that combines deep learning, a lightweight nature, and an attention mechanism to address the shortcomings of current classification methods. The research object is a multi-succulent plant image dataset, covering multi-succulent plants from various families.