Journal of Artificial Intelligence and Capsule Networks
IRO Journals
Volume 7 — Issue 1 — March 2025

Deep Learning CNN Models for Diseases Classification in Cauliflower Leaves

https://doi.org/10.36548/jaicn.2025.1.003
Pages: 32-50
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Trailokya Raj Ojha
Department of Computer Science and Engineering, Kathmandu University, Nepal
Prajwal Chaudhary
Macquarie University, Australia
Sujan Sharma
Fusemachines, Nepal
March 2025
Volume 7 — Issue 1
Journal of Artificial Intelligence and Capsule Networks
PDF

How to Cite

Ojha, Trailokya Raj, Prajwal Chaudhary, and Sujan Sharma. 2025. “Deep Learning CNN Models for Diseases Classification in Cauliflower Leaves”. Journal of Artificial Intelligence and Capsule Networks 7 (1): 32-50. https://doi.org/10.36548/jaicn.2025.1.003.
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Keywords

— CNN
— Cauliflower
— Transfer Learning
— ResNet50
— NASNet Mobile
— Inception V3
Published: 01-04-2025

Abstract

Cauliflower, a widely consumed vegetable valued for its nutrition in cooking, encounters significant agricultural difficulties because of the presence of various diseases that have an adverse impact on its quality and production. Early detection of these diseases is essential for timely plant treatment and increased production. This study presents a novel approach for detecting cauliflower leaf disease using deep learning techniques, taking use of the advances in deep learning for image classification. The study utilizes a dataset consisting of images of healthy leaves and affected leaves by widespread diseases such as Alternaria Leaf Spot, Black Rot, Cabbage Aphid, and Cabbage Looper. A pre-trained convolutional neural network (CNN) architecture is optimized and customized for this particular study in order to achieve disease classification. The proposed study has concentrated on fine-tuning the hyperparameters for the commonly used models such as NASNet Mobile, ResNet50, and Inception V3. The dataset used in this research contains 729 images of cauliflower leaves collected manually with the aid of a mobile phone camera from different farm fields in the Bhaktapur district of Nepal. Several performance metrics, such as accuracy, precision, and recall were used to evaluate the model’s performance. The experimental result shows that the ResNet50 has better performance with an accuracy of 93.47% compared to other models NASNet Mobile and Inception V3.

References

  1. Giri, H.N., 2020. Growth, yield and post harvest quality of late season varieties of cauliflower at Rampur, Chitwan. Journal of Agriculture and Forestry University, pp.169-175. https://doi.org/10.3126/jafu.v4i1.47066
  2. MoAD, 2015. Statistical information on Nepalese Agriculture. Agri-business Promotion and Statistics Division, MoAD, Singha Durbar, Kathmandu, Nepal.
  3. Keck, A.S. and Finley, J.W., 2004. Cruciferous vegetables: cancer protective mechanisms of glucosinolate hydrolysis products and selenium. Integrative Cancer Therapies, 3(1), pp.5-12.
  4. Mohammadpoor, M., Nooghabi, M. G., and Ahmedi, Z., 2020. An Intelligent Technique for Grape Fanleaf Virus Detection. International Journal of Interactive Multimedia and Artificial Intelligence, 6, pp.62–67. https://doi.org/10.9781/ijimai.2020.02.001
  5. Meng-Yuan, Z., Hong-Bing, Y. and Zhi-Wei, L., 2019. Early detection and identification of rice sheath blight disease based on hyperspectral image and chlorophyll content. Spectroscopy and Spectral Analysis, 39(6), pp.1898-1904.
  6. Liu, B., Ding, Z., Tian, L., He, D., Li, S. and Wang, H., 2020. Grape leaf disease identification using improved deep convolutional neural networks. Frontiers in Plant Science, 11, p.1082. https://doi.org/10.3389/fpls.2020.01082
  7. Dawod, R.G. and Dobre, C., 2022. Upper and lower leaf side detection with machine learning methods. Sensors, 22(7), p.2696. https://doi.org/10.3390/s22072696
  8. Mhathesh, T.S.R., Andrew, J., Martin Sagayam, K. and Henesey, L., 2021. A 3D convolutional neural network for bacterial image classification. In Intelligence in Big Data Technologies—Beyond the Hype: Proceedings of ICBDCC 2019 (pp. 419-431). Springer Singapore. https://doi.org/10.1007/978-981-15-5285-4_42
  9. Maria, S.K., Taki, S.S., Mia, M.J., Biswas, A.A., Majumder, A. and Hasan, F., 2022. Cauliflower disease recognition using machine learning and transfer learning. In Smart Systems: Innovations in Computing: Proceedings of SSIC 2021 (pp. 359-375). Springer Singapore. https://doi.org/10.1007/978-981-16-2877-1_33
  10. Too, E.C., Yujian, L., Njuki, S. and Yingchun, L., 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, pp.272-279. https://doi.org/10.1016/j.compag.2018.03.032
  11. Upadhyay, S.K. and Kumar, A., 2022. A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology, pp.1-15. https://doi.org/10.1007/s41870-021-00817-5
  12. Panchal, A.V., Patel, S.C., Bagyalakshmi, K., Kumar, P., Khan, I.R. and Soni, M., 2023. Image-based plant diseases detection using deep learning. Materials Today: Proceedings, 80, pp.3500-3506. https://doi.org/10.1016/j.matpr.2021.07.281
  13. Narayanan, K.L., Krishnan, R.S., Robinson, Y.H., Julie, E.G., Vimal, S., Saravanan, V. and Kaliappan, M., 2022. Banana plant disease classification using hybrid convolutional neural network. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9153699
  14. Hamuda, E., Mc Ginley, B., Glavin, M. and Jones, E., 2017. Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and electronics in agriculture, 133, pp.97-107. https://doi.org/10.1016/j.compag.2016.11.021
  15. Zhang, S., Huang, W. and Zhang, C., 2019. Three-channel convolutional neural networks for vegetable leaf disease recognition. Cognitive Systems Research, 53, pp.31-41. https://doi.org/10.1016/j.cogsys.2018.04.006
  16. Wagh, T.A., Samant, R.M., Gujarathi, S.V. and Gaikwad, S.B., 2019. Grapes leaf disease detection using convolutional neural network. Int. J. Comput. Appl, 178(20). https://doi.org/10.5120/ijca2019918982
  17. Jadhav, S.B., Udupi, V.R. and Patil, S.B., 2021. Identification of plant diseases using convolutional neural networks. International Journal of Information Technology, 13(6), pp.2461-2470. https://doi.org/10.1007/978-3-030-51859-2_65.
  18. Abayomi‐Alli, O.O., Damaševičius, R., Misra, S. and Maskeliūnas, R., 2021. Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning. Expert Systems, 38(7), p.e12746. https://doi.org/10.1111/exsy.12746
  19. Prodeep, A.R., Hoque, A.M., Kabir, M.M., Rahman, M.S. and Mridha, M.F., 2022, March. Plant disease identification from leaf images using deep CNN’S efficientnet. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 523-527). IEEE. https://doi.org/10.1109/DASA54658.2022.9765063
  20. Gokulnath, B.V., Gandhi, U. D., 2021. Identifying and classifying plant disease using resilient LF-CNN. Ecological Informatics, 63, p.101283. https://doi.org/10.1016/j.ecoinf.2021.101283
  21. Anh, P.T. and Duc, H.T.M., 2021, October. A benchmark of deep learning models for multi-leaf diseases for edge devices. In 2021 International Conference on Advanced Technologies for Communications (ATC) (pp. 318-323). IEEE. https://doi.org/10.1109/ATC52653.2021.9598196
  22. Kabir, M.M., Ohi, A.Q. and Mridha, M.F., 2021. A multi-plant disease diagnosis method using convolutional neural network. Computer Vision and Machine Learning in Agriculture, pp.99-111. https://doi.org/10.1007/978-981-33-6424-0_7
  23. Astani, M., Hasheminejad, M. and Vaghefi, M., 2022. A diverse ensemble classifier for tomato disease recognition. Computers and Electronics in Agriculture, 198, p.107054. https://doi.org/10.1016/j.compag.2022.107054
  24. Eunice, J., Popescu, D.E., Chowdary, M.K. and Hemanth, J., 2022. Deep learning-based leaf disease detection in crops using images for agricultural applications. Agronomy, 12(10), p.2395. https://doi.org/10.3390/agronomy12102395
  25. Liu, B., Ding, Z., Tian, L., He, D., Li, S. and Wang, H., 2020. Grape leaf disease identification using improved deep convolutional neural networks. Frontiers in Plant Science, 11, p.1082. https://doi.org/10.3389/fpls.2020.01082
  26. Rajbongshi, A., Islam, M.E., Mia, M.J., Sakif, T.I. and Majumder, A., 2022. A comprehensive investigation to cauliflower diseases recognition: an automated machine learning approach. Int. J. Adv. Sci. Eng. Inform. Technol.(IJASEIT), 12(1), pp.32-41. https://doi.org/10.18517/ijaseit.12.1.15189
  27. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H. and He, Q., 2020. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), pp.43-76. https://doi.org/10.1109/JPROC.2020.3004555
  28. He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90
  29. Zoph, B., Vasudevan, V., Shlens, J. and Le, Q.V., 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8697-8710). https://doi.org/10.1109/CVPR.2018.00907
  30. Radhika, K., Devika, K., Aswathi, T., Sreevidya, P., Sowmya, V. and Soman, K.P., 2020. Performance analysis of NASNet on unconstrained ear recognition. Nature inspired computing for data science, pp.57-82. https://doi.org/10.1007/978-3-030-33820-6_3
  31. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). https://doi.org/10.1109/CVPR.2015.7298594