# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 1 Introduction
The core of the research is to design a model based on a multi-branch structure, which aims to more accurately capture the key details of multi-succulent plants, enhance category distinction, and improve the generalization ability of the model. The innovations of this model mainly include the following two aspects:  
(1) Spatial-channel attention mechanism (CBAM): The CBAM module (Convolutional Block Attention Module) enhances feature expression through a combination of channel and spatial attention. The channel attention module introduces global average pooling and max pooling to generate weights for different channels and multiply them with the original features to enhance the feature expression of key channels. Spatial attention combines the average pooling and maximum pooling results of the input features, generates a spatial attention map through a \(7\times 7\) convolution, and then multiplies it with the original features to ensure that the network can focus on important spatial locations. The optimization and integration of the CBAM module improves the model's feature extraction ability and further alleviates the classification difficulties caused by the similar morphological characteristics of succulents. (2) Lightweight Inception Module: The lightweight Inception module replaces the original \(5\times 5\) convolution with two \(3\times 3\) convolutions. This design significantly reduces the computational complexity and parameters while maintaining the same receptive field. In addition, the number of convolution channels in the module is reduced to further reduce the amount of computation. Compared with the original Inception module, the lightweight module integrates CBAM, which adds an attention mechanism to the output feature map after stitching, making the feature representation more accurate. These modifications make the lightweight Inception module not only have a lower computational cost, but also retain good feature extraction capabilities, making it suitable for application scenarios with limited computing resources. In this study, GoogLeNetCBAM is chosen as the model architecture, mainly based on its advantages in feature extraction, parameter optimization, and overfitting prevention. GoogLeNet adopts the Inception module with a powerful multi-scale feature extraction ability, which can extract rich image features under different sensory fields, especially in the case of succulent plants, considering the large number of species and high similarity between some categories, which helps the model capture more discriminative features and improve the classification accuracy. Meanwhile, the CBAM attention mechanism introduced by the model combines channel attention and spatial attention, which can effectively guide the model to focus on the key feature regions and inhibit the interference of irrelevant information, thus further improving the generalization ability of the model. To further alleviate the overfitting problem in small sample scenarios, we introduce various optimization strategies in the model training process. These include: data enhancement to enrich the diversity of data samples and enhance the model's adaptability to image transformations; the introduction of dropout (\(p\) = 0.3) in the fully connected layer to reduce neuron co-adaptation and improve the robustness of the model; the addition of weight decay (Weight Decay = \(1\times 10^{-4}\)) in the Adam optimizer to limit the amplitude of the parameters and prevent overfitting caused by too-large model parameters; and the introduction of an early stopping strategy to prevent overfitting caused by too-large model parameters. The early stop strategy (patience = 15) is introduced to stop training when the accuracy of the validation set is not improved in consecutive rounds, to avoid the model from overfitting due to overtraining.