# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 1 Introduction  
In recent years, convolutional neural networks (CNNs), as a core method of deep learning technology, have achieved remarkable results in image classification, object detection, and other fields. CNNs extract multi-scale features of images through multiple layers of convolution and pooling operations and perform well in tasks such as plant classification [1, 2, 3]. In plant classification, researchers have widely explored strategies such as multi-scale feature extraction, transfer learning, and ensemble classifiers, which have significantly improved model performance. For example, [PERSON] et al. [4] proposed the multi-scale fusion convolutional neural network (MSF-CNN), which achieved excellent performance on the MalayaKew Leaf [5] and LeafSnap [6] datasets, verifying the effectiveness of multi-scale features. [PERSON] et al. [7] used AlexNet transfer learning and image distortion techniques to improve recognition accuracy on the Flavia leaf dataset. [PERSON] et al. [8] proposed the D-Leaf model, which combines classifiers such as support vector machines (SVMs), artificial neural networks (ANNs) and k-nearest neighbors (k-NNs), toachieve more accurate plant species identification. [PERSON] et al. [9] combined VGG16, ResNet50, and YOLOV7 to achieve an accuracy of over 98% in classifying fruit images, further demonstrating the powerful potential of deep learning in plant image analysis. The above studies have laid a solid foundation for applying CNN in plant image recognition. On this basis, in recent years, researchers have conducted in-depth explorations around the structural optimization and performance improvement of CNNs, with particularly groundbreaking progress made in 2024 and 2025. In 2024, [PERSON] et al. [10] proposed an image classification method based on CNNs, achieving an accuracy rate of 98% on the MNIST dataset, further demonstrating the powerful ability of CNNs to improve image classification performance. In 2025, [PERSON] et al. [11] proposed the scalable quantum convolutional neural network (SQCNN) model, which uses quantum circuits to extract features in parallel, achieving a classification accuracy of 99.79% on the MNIST and Fashion MNIST datasets. This is significantly better than existing quantum neural network models and shows strong generalization capabilities. Meanwhile, the CA-MFE model proposed by [PERSON] et al. [12] combines deformable CNN with an attention mechanism to construct a multi-scale graph neural network (GNN), which effectively extracts multi-scale local and global features and significantly improves image classification performance. It also performs well on both the mini-ImageNet and tiered-ImageNet datasets. In addition, the CBAM-SqueezeNet model proposed by [PERSON] et al. [13] further optimizes the feature extraction module of the CNN by introducing channel and spatial attention mechanisms, achieving more accurate and efficient detection of target grasping by robotic arms. This model achieved a grasp detection accuracy of 94.8% and 96.4% on the Cornell Grasping Dataset and Jacquard Dataset, respectively, and a success rate of 93% in actual robotic grasping experiments. The inference speed reached 15 ms, which fully balances model accuracy and speed. These research results show that CNN models that combine quantum characteristics, attention mechanisms, and multi-scale feature extraction have made significant breakthroughs in the past two years, further promoting the development of image classification and related applications. Despite the many advances made by CNNs in the field of image analysis, they still face significant challenges in the task of succulent plant classification. Succulents come in a wide variety of species, with highly similar morphological characteristics, and are significantly affected by their growth environment [14; 15]. There are more than 12,000 species of succulents known worldwide, belonging to about 80 families [16], but only about 100 species are sold as ornamental potted plants in the Chinese market.