Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
Aiming at the model overfitting problem caused by limited datasets and visual complexity in succulent plant classification tasks, this study proposes a GoogLeNet classification method based on lightweighting and improving the Convolutional Block attention module (CBAM). Meanwhile, batch normalizatio...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3730 |
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| author | Xingyu Tong Zhihong Liang Fangrong Liu |
| author_facet | Xingyu Tong Zhihong Liang Fangrong Liu |
| author_sort | Xingyu Tong |
| collection | DOAJ |
| description | Aiming at the model overfitting problem caused by limited datasets and visual complexity in succulent plant classification tasks, this study proposes a GoogLeNet classification method based on lightweighting and improving the Convolutional Block attention module (CBAM). Meanwhile, batch normalization (BN) operations are added after each convolutional layer to accelerate network convergence and improve model stability. In addition, the model’s ability to extract key features is enhanced by integrating the channel and spatial attention mechanisms of the CBAM attention module. Experimental results show that the improved lightweight GoogLeNet achieves 99.4% classification accuracy on the validation set, effectively mitigates the overfitting problem, and maintains high computational efficiency in resource-constrained environments. The model parameters and computational complexity are significantly reduced by streamlining the Inception modules from nine to seven and introducing depth-separable convolution. To further validate the model robustness, this study extends the dataset via data augmentation methods, and the experiments show that the improved model still maintains stable performance in small dataset environments, demonstrating its advantages in data-scarce scenarios. This study provides an effective solution for the task of succulent plant classification, which has significant application value. Future research will focus on further optimization of the model structure to continuously improve the classification accuracy and robustness. |
| format | Article |
| id | doaj-art-cfbd1646d22a45ef8879945c64e5b844 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-cfbd1646d22a45ef8879945c64e5b8442025-08-20T02:17:00ZengMDPI AGApplied Sciences2076-34172025-03-01157373010.3390/app15073730Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention MechanismXingyu Tong0Zhihong Liang1Fangrong Liu2College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, ChinaAiming at the model overfitting problem caused by limited datasets and visual complexity in succulent plant classification tasks, this study proposes a GoogLeNet classification method based on lightweighting and improving the Convolutional Block attention module (CBAM). Meanwhile, batch normalization (BN) operations are added after each convolutional layer to accelerate network convergence and improve model stability. In addition, the model’s ability to extract key features is enhanced by integrating the channel and spatial attention mechanisms of the CBAM attention module. Experimental results show that the improved lightweight GoogLeNet achieves 99.4% classification accuracy on the validation set, effectively mitigates the overfitting problem, and maintains high computational efficiency in resource-constrained environments. The model parameters and computational complexity are significantly reduced by streamlining the Inception modules from nine to seven and introducing depth-separable convolution. To further validate the model robustness, this study extends the dataset via data augmentation methods, and the experiments show that the improved model still maintains stable performance in small dataset environments, demonstrating its advantages in data-scarce scenarios. This study provides an effective solution for the task of succulent plant classification, which has significant application value. Future research will focus on further optimization of the model structure to continuously improve the classification accuracy and robustness.https://www.mdpi.com/2076-3417/15/7/3730succulentslightweightingCBAM attention mechanismGoogLeNetclassification and recognition |
| spellingShingle | Xingyu Tong Zhihong Liang Fangrong Liu Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism Applied Sciences succulents lightweighting CBAM attention mechanism GoogLeNet classification and recognition |
| title | Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism |
| title_full | Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism |
| title_fullStr | Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism |
| title_full_unstemmed | Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism |
| title_short | Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism |
| title_sort | succulent plant image classification based on lightweight googlenet with cbam attention mechanism |
| topic | succulents lightweighting CBAM attention mechanism GoogLeNet classification and recognition |
| url | https://www.mdpi.com/2076-3417/15/7/3730 |
| work_keys_str_mv | AT xingyutong succulentplantimageclassificationbasedonlightweightgooglenetwithcbamattentionmechanism AT zhihongliang succulentplantimageclassificationbasedonlightweightgooglenetwithcbamattentionmechanism AT fangrongliu succulentplantimageclassificationbasedonlightweightgooglenetwithcbamattentionmechanism |