Comparison of Standard and Squeeze-and-Excitation Enhanced DenseNet Architectures for Tomato Leaf Disease Classification Using Data Augmentation
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Abstract
The advancement of deep learning has significantly improved the automation of plant disease detection through image classification. This study compares the performance of standard DenseNet121 and an enhanced version incorporating Squeeze-and-Excitation (SE) blocks for classifying tomato leaf diseases. A dataset derived from PlantVillage was used, covering multiple disease categories and healthy leaves. To improve generalization, extensive data augmentation techniques were applied. Both architectures were implemented and trained using PyTorch, with evaluation metrics including accuracy, precision, recall, F1-score, and inference time. The experimental results demonstrate that DenseNet121-SE significantly outperforms the standard DenseNet121, achieving a classification accuracy of 99.00%. The integration of SE blocks allows the model to recalibrate channel-wise features adaptively, enhancing sensitivity to important patterns while maintaining computational efficiency. This study highlights the effectiveness of attention mechanisms and data augmentation in improving classification performance and supports their practical application in intelligent agriculture systems.
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How to Cite
Andriani, T., & Nainggolan, I. (2025). Comparison of Standard and Squeeze-and-Excitation Enhanced DenseNet Architectures for Tomato Leaf Disease Classification Using Data Augmentation. Instal : Jurnal Komputer, 17(08), 525–533. https://doi.org/10.54209/jurnalinstall.v17i08.429
References
[1] I. Khan, S. S. Sohail, D. Ø. Madsen, and B. K. Khare, “Deep transfer learning for fine-grained maize leaf disease classification,” J. Agric. Food Res., vol. 16, no. March, 2024, doi: 10.1016/j.jafr.2024.101148.
[2] M. Umar, S. Altaf, S. Ahmad, H. Mahmoud, A. S. N. Mohamed, and R. Ayub, “Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition with CNN and Improved YOLOv7,” IEEE Access, vol. 12, no. April, pp. 49167–49183, 2024, doi: 10.1109/ACCESS.2024.3383154.
[3] M. Bakr, S. Abdel-Gaber, M. Nasr, and M. Hazman, “Tomato Disease Detection Model Based on Densenet and Transfer Learning,” Appl. Comput. Sci., vol. 18, no. 2, pp. 56–70, 2022, doi: 10.35784/acs-2022-13.
[4] P. B. N. Simangunsong and P. Sihombing, “Densenet Development With Squeeze-and-Excitation Block for Tomato Plant Disease Classification,” Eastern-European J. Enterp. Technol., vol. 2, no. 2(134), pp. 28–38, 2025, doi: 10.15587/1729-4061.2025.323176.
[5] J. D. D. Jayaseeli, J. Briskilal, C. Fancy, and V. Vaitheeshwaran, “An intelligent framework for skin using fusion of Squeeze-Excitation- DenseNet with Metaheuristic- driven ensemble deep learning models,” no. 2025.
[6] A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, vol. 9, pp. 71194–71209, 2021, doi: 10.1109/ACCESS.2021.3079204.
[7] M. K. A. Mazumder, M. M. Kabir, A. Rahman, M. Abdullah-Al-Jubair, and M. F. Mridha, “DenseNet201Plus: Cost-effective transfer-learning architecture for rapid leaf disease identification with attention mechanisms,” Heliyon, vol. 10, no. 15, p. e35625, 2024, doi: 10.1016/j.heliyon.2024.e35625.
[8] K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, 2022, doi: 10.1016/j.gltp.2022.04.020.
[9] T. Kumar, A. Mileo, R. Brennan, and M. Bendechache, “Image Data Augmentation Approaches: A Comprehensive Survey and Future directions,” vol. 12, no. December, 2023, doi: 10.1109/ACCESS.2024.3470122.
[10] M. Yang, T. Ma, Q. Tian, Y. Tian, A. Al-Dhelaan, and M. Al-Dhelaan, “Aggregated squeeze-and-excitation transformations for densely connected convolutional networks,” Vis. Comput., vol. 38, no. 8, pp. 2661–2674, 2022, doi: 10.1007/s00371-021-02144-z.
[11] H. Deng, D. Luo, Z. Chang, H. Li, and X. Yang, “Rahc_gan: A data augmentation method for tomato leaf disease recognition,” Symmetry (Basel)., vol. 13, no. 9, 2021, doi: 10.3390/sym13091597.
[12] M. Li et al., “FWDGAN-based data augmentation for tomato leaf disease identification,” Comput. Electron. Agric., vol. 194, p. 106779, 2022, doi: https://doi.org/10.1016/j.compag.2022.106779.
[13] M. S. Srivathsan, S. A. Jenish, K. Arvindhan, and R. Karthik, “An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification,” Sci. Rep., vol. 15, no. 1, pp. 1–16, 2025, doi: 10.1038/s41598-025-95985-w.
[14] G. Sambasivam, G. Prabu kanna, M. S. Chauhan, P. Raja, and Y. Kumar, “A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases,” Sci. Rep., vol. 15, no. 1, pp. 1–24, 2025, doi: 10.1038/s41598-025-90646-4.
[15] S. Duhan et al., “Investigating attention mechanisms for plant disease identification in challenging environments,” Heliyon, vol. 10, no. 9, p. e29802, 2024, doi: 10.1016/j.heliyon.2024.e29802.
[16] L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning - A Review,” IEEE Access, vol. 9, no. Ccv, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
[17] A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Front. Plant Sci., vol. 15, no. March, pp. 1–20, 2024, doi: 10.3389/fpls.2024.1356260.
[18] Y. Hou, Z. Wu, X. Cai, and T. Zhu, “The application of improved densenet algorithm in accurate image recognition,” Sci. Rep., vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-58421-z.
[19] S. Shaik and S. Kirthiga, “Automatic Modulation Classification using DenseNet,” in 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), 2021, pp. 301–305, doi: 10.1109/ICCCSP52374.2021.9465520.
[20] S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony, and M. H. Kabir, “Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification,” IEEE Access, vol. 10, no. July, pp. 68868–68884, 2022, doi: 10.1109/ACCESS.2022.3187203.
[21] G. Pinto, Z. Wang, A. Roy, T. Hong, and A. Capozzoli, “Advances in Applied Energy Transfer learning for smart buildings : A critical review of algorithms , applications , and future perspectives,” Adv. Appl. Energy, vol. 5, no. January, p. 100084, 2022, doi: 10.1016/j.adapen.2022.100084.
[22] A. Jlassi, A. Elaoud, H. Ghazouani, and W. Barhoumi, “Potato Leaf Disease Classification Using Transfer Learning and Reweighting-Based Training with Imbalanced Data,” SN Comput. Sci., vol. 5, no. 8, 2024, doi: 10.1007/s42979-024-03334-x.
[23] J. Uddin, “Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images,” 2024.
[24] S. Eeg, “An Attention-Based Deep Learning Approach for Sleep Stage Classification With,” vol. 29, pp. 809–818, 2021, doi: 10.1109/TNSRE.2021.3076234.
[25] Y. Li, “Classi fi cation of Vernacular Landscape Elements and Design Applications Based on Deep Learning Fully Convolutional Neural Networks,” vol. 2025, 2025, doi: 10.1155/adce/9363110.
[26] N. Peladarinos, D. Piromalis, V. Cheimaras, E. Tserepas, R. A. Munteanu, and P. Papageorgas, “Enhancing Smart Agriculture by Implementing Digital Twins : A Comprehensive Review,” pp. 1–38, 2023.
[27] M. Jiang, C. Feng, X. Fang, Q. Huang, C. Zhang, and X. Shi, “Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network,” pp. 1–14, 2023.
[28] Y. Zhang, X. Zhang, Z. Li, X. Li, and Z. Wang, “Hierarchical intelligent lithology recognition for thin section images using enhanced DenseNet,” Earth Sci. Informatics, vol. 18, no. 1, 2025, doi: 10.1007/s12145-024-01663-2.
[2] M. Umar, S. Altaf, S. Ahmad, H. Mahmoud, A. S. N. Mohamed, and R. Ayub, “Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition with CNN and Improved YOLOv7,” IEEE Access, vol. 12, no. April, pp. 49167–49183, 2024, doi: 10.1109/ACCESS.2024.3383154.
[3] M. Bakr, S. Abdel-Gaber, M. Nasr, and M. Hazman, “Tomato Disease Detection Model Based on Densenet and Transfer Learning,” Appl. Comput. Sci., vol. 18, no. 2, pp. 56–70, 2022, doi: 10.35784/acs-2022-13.
[4] P. B. N. Simangunsong and P. Sihombing, “Densenet Development With Squeeze-and-Excitation Block for Tomato Plant Disease Classification,” Eastern-European J. Enterp. Technol., vol. 2, no. 2(134), pp. 28–38, 2025, doi: 10.15587/1729-4061.2025.323176.
[5] J. D. D. Jayaseeli, J. Briskilal, C. Fancy, and V. Vaitheeshwaran, “An intelligent framework for skin using fusion of Squeeze-Excitation- DenseNet with Metaheuristic- driven ensemble deep learning models,” no. 2025.
[6] A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, vol. 9, pp. 71194–71209, 2021, doi: 10.1109/ACCESS.2021.3079204.
[7] M. K. A. Mazumder, M. M. Kabir, A. Rahman, M. Abdullah-Al-Jubair, and M. F. Mridha, “DenseNet201Plus: Cost-effective transfer-learning architecture for rapid leaf disease identification with attention mechanisms,” Heliyon, vol. 10, no. 15, p. e35625, 2024, doi: 10.1016/j.heliyon.2024.e35625.
[8] K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, 2022, doi: 10.1016/j.gltp.2022.04.020.
[9] T. Kumar, A. Mileo, R. Brennan, and M. Bendechache, “Image Data Augmentation Approaches: A Comprehensive Survey and Future directions,” vol. 12, no. December, 2023, doi: 10.1109/ACCESS.2024.3470122.
[10] M. Yang, T. Ma, Q. Tian, Y. Tian, A. Al-Dhelaan, and M. Al-Dhelaan, “Aggregated squeeze-and-excitation transformations for densely connected convolutional networks,” Vis. Comput., vol. 38, no. 8, pp. 2661–2674, 2022, doi: 10.1007/s00371-021-02144-z.
[11] H. Deng, D. Luo, Z. Chang, H. Li, and X. Yang, “Rahc_gan: A data augmentation method for tomato leaf disease recognition,” Symmetry (Basel)., vol. 13, no. 9, 2021, doi: 10.3390/sym13091597.
[12] M. Li et al., “FWDGAN-based data augmentation for tomato leaf disease identification,” Comput. Electron. Agric., vol. 194, p. 106779, 2022, doi: https://doi.org/10.1016/j.compag.2022.106779.
[13] M. S. Srivathsan, S. A. Jenish, K. Arvindhan, and R. Karthik, “An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification,” Sci. Rep., vol. 15, no. 1, pp. 1–16, 2025, doi: 10.1038/s41598-025-95985-w.
[14] G. Sambasivam, G. Prabu kanna, M. S. Chauhan, P. Raja, and Y. Kumar, “A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases,” Sci. Rep., vol. 15, no. 1, pp. 1–24, 2025, doi: 10.1038/s41598-025-90646-4.
[15] S. Duhan et al., “Investigating attention mechanisms for plant disease identification in challenging environments,” Heliyon, vol. 10, no. 9, p. e29802, 2024, doi: 10.1016/j.heliyon.2024.e29802.
[16] L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning - A Review,” IEEE Access, vol. 9, no. Ccv, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
[17] A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Front. Plant Sci., vol. 15, no. March, pp. 1–20, 2024, doi: 10.3389/fpls.2024.1356260.
[18] Y. Hou, Z. Wu, X. Cai, and T. Zhu, “The application of improved densenet algorithm in accurate image recognition,” Sci. Rep., vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-58421-z.
[19] S. Shaik and S. Kirthiga, “Automatic Modulation Classification using DenseNet,” in 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), 2021, pp. 301–305, doi: 10.1109/ICCCSP52374.2021.9465520.
[20] S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony, and M. H. Kabir, “Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification,” IEEE Access, vol. 10, no. July, pp. 68868–68884, 2022, doi: 10.1109/ACCESS.2022.3187203.
[21] G. Pinto, Z. Wang, A. Roy, T. Hong, and A. Capozzoli, “Advances in Applied Energy Transfer learning for smart buildings : A critical review of algorithms , applications , and future perspectives,” Adv. Appl. Energy, vol. 5, no. January, p. 100084, 2022, doi: 10.1016/j.adapen.2022.100084.
[22] A. Jlassi, A. Elaoud, H. Ghazouani, and W. Barhoumi, “Potato Leaf Disease Classification Using Transfer Learning and Reweighting-Based Training with Imbalanced Data,” SN Comput. Sci., vol. 5, no. 8, 2024, doi: 10.1007/s42979-024-03334-x.
[23] J. Uddin, “Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images,” 2024.
[24] S. Eeg, “An Attention-Based Deep Learning Approach for Sleep Stage Classification With,” vol. 29, pp. 809–818, 2021, doi: 10.1109/TNSRE.2021.3076234.
[25] Y. Li, “Classi fi cation of Vernacular Landscape Elements and Design Applications Based on Deep Learning Fully Convolutional Neural Networks,” vol. 2025, 2025, doi: 10.1155/adce/9363110.
[26] N. Peladarinos, D. Piromalis, V. Cheimaras, E. Tserepas, R. A. Munteanu, and P. Papageorgas, “Enhancing Smart Agriculture by Implementing Digital Twins : A Comprehensive Review,” pp. 1–38, 2023.
[27] M. Jiang, C. Feng, X. Fang, Q. Huang, C. Zhang, and X. Shi, “Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network,” pp. 1–14, 2023.
[28] Y. Zhang, X. Zhang, Z. Li, X. Li, and Z. Wang, “Hierarchical intelligent lithology recognition for thin section images using enhanced DenseNet,” Earth Sci. Informatics, vol. 18, no. 1, 2025, doi: 10.1007/s12145-024-01663-2.
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