Evaluation of SE, ECA, and CA Modules in ResNet-50 for the Classification of Grape Leaf Diseases
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Context: Plant diseases pose a major threat to agricultural production, as they hinder crop growth and significantly reduce yields. In this context, the automatic detection of diseases using artificial intelligence techniques represents an effective solution. Objective: This work presents a comparative study on the integration of attention mechanisms into the ResNet-50 architecture, applied to the classification of grape leaf diseases. Method: Three attention modules were considered: SE (Squeeze-and-Excitation), ECA (Efficient Channel Attention), and CA (Coordinate Attention), each integrated separately into the model to evaluate its impact. Results: The performance of the different configurations was assessed by considering the quality of the results, the model complexity, and the processing speed. Experimental results show that the ECA module achieved the best performance with a reduced number of parameters, followed by SE and then CA. Conclusion: This study highlights the contribution of attention mechanisms in improving the effectiveness of CNN architectures for plant disease detection.
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[1] V. G. Krishnan, J. R. V. P. Deepa, P. V. Rao, V. Divya, and S. Kaviarasan, “An Automated Segmentation and Classification Model for Banana Leaf Disease Detection”, Journal of Applied Biology and Biotechnology, vol. 10, no. 1, 2022, pp. 213–220. https://doi.org/10.7324/JABB.2021.100126
[2] W. B. Demilie, “Plant Disease Detection and Classification Techniques: A Comparative Study of the Performances,” Journal of Big Data, vol. 11, no. 1, 2024, p. 5. https://doi.org/10.1186/s40537-023-00863-9
[3] M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm”, in 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, 2018, pp. 382–385. https://doi.org/10.1109/UBMK.2018.8566635
[4] E. Dhinesh and A. Jagan, “Detection of Leaf Disease Using Principal Component Analysis and Linear Support Vector n Machine,” in 2019 11th International Conference on Advanced Computing (ICoAC), IEEE, 2019, pp. 350–355. https://dx.doi.org/10.1109/ICoAC48765.2019.246866
[5] K. Hilagavathi, K. Kavitha, R. Dhivya Praba, S. V. Arina, and R. C. Sahana, “Detection of Diseases in Sugarcane Using Image Processing Techniques”, Bioscience Biotechnology Research Communications, Special Issue 11, 2020, pp. 109–115. https://doi.org/10.21786/bbrc/13.11/24
[6] O. Attallah, “Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection”, Horticulturae, vol. 9, no. 2, 2023, p. 149. https://doi.org/10.3390/horticulturae9020149
[7] J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7132–7141. https://doi.org/10.48550/arXiv.1709.01507
[8] Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11534–11542. https://doi.org/10.1109/CVPR42600.2020.01155
[9] Q. Hou, D. Zhou, and J. Feng, “Coordinate Attention for Efficient Mobile Network Design,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 13713–13722. https://doi.org/10.48550/arXiv.2103.02907
[10] K. Thilagavathi, K. Kavitha, R. Dhivya Praba, S. V. Arina, and R. C. Sahana, “Detection of Diseases in Sugarcane Using Image Processing Techniques”, Bioscience Biotechnology Research Communications, Special Issue 11, 2020, pp. 109–115. http://dx.doi.org/10.21786/bbrc/13.11/24
[11] R. Anand, S. Veni, and J. Aravinth, “An Application of Image Processing Techniques for Detection of Diseases on Brinjal Leaves Using K-Means Clustering Method” in 2016 International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, 2016, pp. 1–6. https://doi.org/10.1109/ICRTIT.2016.7569531
[12] W. Albattah, M. Nawaz, A. Javed, M. Masood, and S. Albahli, “A Novel Deep Learning Method for Detection and Classification of Plant Diseases”, Complex & Intelligent Systems, 2022, pp. 1–18. https://doi.org/10.1007/s40747-021-00536-1
[13] M. S. A. M. Al-gaashani, F. Shang, M. S. A. Muthanna, M. Khayyat, and A. A. Abd El-Latif, “Tomato Leaf Disease Classification by Exploiting Transfer Learning and Feature Concatenation”, IET Image Processing, vol. 16, no. 3, 2022, pp. 913–925. https://doi.org/10.1049/ipr2.12397
[14] P. Nayar, S. Chhibber, and A. K. Dubey, “An Efficient Algorithm for Plant Disease Detection Using Deep Convolutional Networks” in 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, 2022, pp. 156–160. https://doi.org/10.1109/CICN56167.2022.10008235
[15] S. Sharma and M. Vardhan, “MFANet: Multi-level Features and Enhancement Operations with the Attention Network for Plant Disease Classification”, Arab Journal of Science and Engineering, 2025. https://doi.org/101007/s13369-025-10833-3
[16] M. Asghar, Z. F. Khan, M. Ramzan, M. A. Khan, J. Baili, Y. Zhang, Y. Nam, and Y. C. Nam, “A lightweight hybrid model for scalable and robust plant leaf disease classification”, Scientific Reports, vol. 15, no. 1, 2025, p. 32353. https://doi.org/10.1038/s41-025-08788-4
[17] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 25, 2012. https://doi.org/10.1145/3065386
[18] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition” arXiv preprint arXiv:1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556
[19] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90
[20] Grapevine Disease Dataset https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original