skip to main content

Optimizing VGG16 Architecture with Bayesian Hyperparameter Tuning for Tomato Leaf Disease Classification

Departement of Informatics, Universitas Diponegoro, Jl. Prof Jacob Rais, Tembalang, Semarang, Indonesia 50275 , Indonesia

Received: 12 May 2025; Revised: 12 Jun 2025; Accepted: 16 Jun 2025; Published: 18 Jun 2025.
Editor(s): Ferda Ernawan
Open Access Copyright (c) 2025 The authors. Published by Department of Informatics Universitas, Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
This study proposes an optimized VGG16 architecture enhanced through Bayesian Optimization to improve the classification of tomato leaf diseases. The modified model integrates tunable parameters such as dropout rates, convolutional filters, and dense units, while maintaining the foundational structure of VGG16. To further refine performance, Bayesian Optimization is employed to search for the most effective combination of hyperparameters. Experiments conducted using the Tomato Leaf Disease Detection dataset demonstrate that the proposed method outperforms the original VGG16 model, achieving a test accuracy of 97.1% compared to 89.0%. These results underline the importance of architecture customization and systematic hyperparameter tuning for domain-specific deep learning tasks in agriculture.
Fulltext View|Download
Keywords: Tomato leaf disease; VGG16; Bayesian Optimization; Deep learning; Image classification; Hyperparameter tuning

Article Metrics:

  1. K. Simonyan and A. Zisserman. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” 3rd International Conference on Learning Representations (ICLR 2015), Computational and Biological Learning Society, pp. 1–14, 2015
  2. https://doi.org/10.48550/arXiv.1409.155
  3. J. Thomkaew and S. Intakosum, “Modified VGG16 with InceptionV3 Modules for Plant Disease Classification”. Procedia Computer Science, 167, pp. 302–309, 2022. https://doi.org/10.1016/j.procs.2020.03.238
  4. M. O. Faruk, M. R. Islam, & M. T. Hasan, “A Deep Learning Model Using Attention Mechanism For Plant Disease Classification”. Computers and Electronics in Agriculture, vol. 205, 107646, 2023. https://doi.org/10.1016/j.compag.2023.107646
  5. M. A. Khan, A. U. Rehman, and A. Mustafa, “Optimized Deep Multimodal Tomato Leaf Disease Classification using Bayesian-Tuned CNN. Applied Sciences, vol. 14, No. 2, 1862, 2024. https://doi.org/10.3390/app14021862
  6. A. Thakur and T. K. Gandhi, “Improvement In Classification Approach in Tomato Leaf Disease Detection using Modified Deep CNN”. Procedia Computer Science, vol. 167, pp. 293–301, 2022. https://doi.org/10.1016/j.procs.2020.03.237
  7. J. Snoek, H. Larochelle, and R. P. Adams, Practical Bayesian Optimization of Machine Learning Algorithms. In Advances in Neural Information Processing Systems, Vol. 25. 2012. https://proceedings.neurips.cc/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf
  8. B. Khan, S. Das, Fahim, N.S. et al. “Bayesian Optimized Multimodal Deep Hybrid Learning Approach for Tomato Leaf Disease Classification”. Sci Rep 14, 21525, 2024. https://doi.org/10.1038/s41598-024-72237-x
  9. S. P. Mohanty, D. P. Hughes, & M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection”. Frontiers in Plant Science, vol. 7, 1419, 2016. https://doi.org/10.3389/fpls.2016.01419
  10. K. P. Ferentinos, “Deep Learning Models for Plant Disease Detectiona nd Diagnosis”. Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018. https://doi.org/10.1016/j.compag.2018.01.009
  11. E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification”. Computers and Electronics in Agriculture, vol. 161, pp. 272–279, 2019. https://doi.org/10.1016/j.compag.2018.03.032
  12. Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model”. Ecological Informatics, vol. 61, 101182, 2021. https://doi.org/10.1016/j.ecoinf.2020.101182
  13. Y. Sutaji and O. Yıldız, “Vision Transformer Meets Convolutional Neural Network for Plant Disease Classification”. Ecological Informatics, 69, 101678, 2022. https://doi.org/10.1016/j.ecoinf.2022.101678
  14. J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using Deep Transfer Learning for Image-Based Plant Disease Identification”. Computers and Electronics in Agriculture, 173, 105393, 2020. https://doi.org/10.1016/j.compag.2020.105393
  15. S. Coulibaly, B. Kamsu-Foguem, and D. Kamissoko, “Deep Learning for Plant Diseases: Detection and Diagnosis”. Computers and Electronics in Agriculture, 182, 105993, 2021. https://doi.org/10.1016/j.compag.2021.105993
  16. R. Prabha and P. R. Chelliah, “Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves using Bayesian Optimization”. Computers and Electronics in Agriculture, 187, 106290, 2022. https://doi.org/10.1016/j.compag.2021.106290
  17. A. Mustafa and M. A. Khan, “Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network”. Applied Sciences, 12(19), pp. 9118, 2022. https://doi.org/10.3390/app12199118
  18. A. S. Abade, P. A. Ferreira, and F. B. Vidal, “Metaheuristic Methods for Hyperparameter Tuning in Deep Learning and Their Application in Plant Leaf Disease Detection: A Survey”. Artificial Intelligence in Agriculture, 6, pp.1–18, 2022. https://doi.org/10.1016/j.aiia.2022.05.001
  19. A. K. Rangarajan and R. Purushothaman, “Tomato Crop Disease Classification using Pre-Trained Deep Learning Algorithm”. Procedia Computer Science, 180, pp. 586–593, 2021. https://doi.org/10.1016/j.procs.2021.01.311
  20. S. T. Y. Ramadan, T. Sakib, F Al Farid, Md.S. Islam, J.B. Abdullah, and Md. R. Bhuiyan, "Improving Wheat Leaf Disease Classification: Evaluating Augmentation Strategies and CNN-Based Models With Limited Dataset," in IEEE Access, vol. 12, pp. 69853-69874, 2024, doi: 10.1109/ACCESS.2024.3397570

Last update:

No citation recorded.

Last update: 2025-07-02 16:11:59

No citation recorded.