A Deep Learning Approach to Multi-class and Multi-label Cassava Leaf Disease Detection Using MobileNetV2 and Image Augmentation

Chika Norah John, Obi Chukwuemeka Nwokonkwo, Anthony Ifeanyi Otuonye, Ikechukwu Ignatius Ayogu

Abstract


This paper presents the development of a deep learning model using pre-trained models to detect multiple cassava leaf diseases using simultaneous multi-class and multi-label classification. Cassava diseases often occur as single or mixed infections, complicating visual diagnosis critical to food security in tropical regions.Existing models, designed primarily for single-label classification, struggle with overlapping symptoms. To address this, a dataset of 10,000 expert- annotated cassava leaf images was compiled from PlantVillage. The MobileNetV2 model, optimized for mobile deployment, was trained using binary cross-entropy loss with Sigmoid activation for Multi-label classification and Categorical cross-entropy loss with Softmax activation. The model achieved 95% accuracy, outperforming NasNet, demonstrating effective real-time, in-field disease diagnosis. This research advances agricultural AI applications by enabling scalable, mobile-compatible cassava disease detection.


Keywords


Cassava leaf disease detection, Multi-class and Multi-label classification, MobileNetV2, Lightweight deep learning

Full Text:

PDF

References


REFERENCES

S. M. Riaz, M. Ahsan, and M. U. Akram, "Diagnosis of cassava leaf diseases and classification using deep learning techniques," in Proc. 2022 16th Int. Conf. Open Source Syst. Technol. (ICOSST), Islamabad, Pakistan, Dec. 2022, pp. 1–7, doi: 10.1109/ICOSST57195.2022.10016854.

J. Liu and X. Wang, “Plant diseases and pests detection based on deep learning: a review,” Plant Methods, vol. 17, no. 1, p. 148, 2021, doi: 10.1186/s13007-021-00722-9.

S. M. Hassan and A. K. Maji, "Plant Disease Identification Using a Novel Convolutional Neural Network," IEEE Access, vol. 10, pp. 5391-5400, 2022, doi: 10.1109/ACCESS.2022.3141371.

N. P. and J. Vijayakumar, “Recognition and classification of cassava leaf diseases using machine learning techniques,” International Journal of Novel Research and Development, vol. 7, no. 4, pp. 783–791, Apr. 2022, ISSN: 2456-4184. [Online]. Available: www.ijnrd.org

Z. Ma, Y. Wang, T. Zhang, H. Wang, Y. Jia, R. Gao, and Z. Su, "Maize leaf disease identification using deep transfer convolutional neural networks," Int. J.Agric. Biol. Eng., vol. 15, no. 5, pp. 187–195, Sep. 2022, doi: 10.25165/j.ijabe.20221505.6658. [Online]. Available: https://www.ijabe.org

P. Alirezazadeh, M. Schirrmann, and F. Stolzenburg, "Improving Deep Learning-based Plant Disease Classification withAttention Mechanism," Gesunde Pflanzen, vol. 74, no. 4, pp. 253–269, Nov. 2022, doi: 10.1007/s10343-022-00796-y. [Online]. Available: https://doi.org/10.1007/s10343-022-00796-y

R.I.Hasan,S.M.Yusuf,andL.Alzubaidi,"ReviewoftheStateoftheArtofDeepLearning forPlant Diseases:ABroadAnalysis and Discussion,"Plants, vol. 9, no.10, p. 1302, Oct. 2020, doi: 10.3390/plants9101302

O. O. Abayomi-Alli, R. Damaševičius, S. Misra, and R. Maskeliūnas, “Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning,” Expert Systems, vol. 38, no. 5, e12746, 2021, doi: 10.1111/exsy.12746.

B. D. Satoto, M. Syarief, and B. K. Khotimah, “Region Proposal Convolutional Neural Network with augmentation to identifying Cassava leaf disease,” 2021 4th InternationalSeminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 212– 217, 2021, doi: 10.1109/ISRITI54043.2021.9702829.

A. A. John, “Identification of Diseases in Cassava Leaves using Convolutional Neural Network,” 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT), pp. 53–58, 2022, doi: 10.1109/CCICT56684.2022.00013.

Y. Zhou, S. Chen, Y. Wang, and W. Huan, “Review of research on lightweight convolutional neural networks,” 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 1401–1407, 2020, doi: 10.1109/ITOEC49072.2020.9141717.

A. S. Tewari, "Lightweight Convolutional Neural Network Model for Cassava Leaf Diseases Classification," SN Computer Science, vol. 5, no. 284, 2024, doi: 10.1007/s42979- 024-02620-y.

K. Thaiyalnayaki and C. Joseph, "Classification of plant disease using SVM and deep learning," Materials Today: Proceedings, 2021, doi: 10.1016/j.matpr.2021.05.029.

M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic, "Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection,"Sensors, vol. 19, no. 19, p. 4184, Jul. 2019. DOI: 10.3390/s19194184.

V. Mall and K. Gupta, “Comparative analysis of classification techniques for credit card fraud detection,” International Research Journal of Computer Science, vol. 9, no. 2, Feb.2022. doi: 10.26562/irjcs.2022.v0902.003. [Online]. Available: https://doi.org/10.26562/irjcs.2022.v0902.003

S. Sarumathi, P. Ranjetha, C. Saraswathy, and S. Gayathri, “Deep neural network algorithms and their role: A systematic review,” International Research Journal of Computer Science, vol. 9, no. 2, pp. 16–25, Feb. 2022. doi: 10.26562/irjcs.2022.v0902.004. [Online]. Available: http://www.irjcs.com/volumes/Vol09/iss-02/04.FBCS10082.pdf

N. P. and J. Vijayakumar, “Recognition and classification of cassava leaf diseases using machine learning techniques,” International Journal of Novel Research and Development,vol. 7, no. 4, pp. 783–791, Apr. 2022,ISSN: 2456-4184. [Online]. Available:www.ijnrd.org

M. Mankal, C. K. Kumar, and S. Nagaraj, “Detection of cassava plant related diseases using deep learning,” International Research Journal of Plant Science, vol. 12, no. 3, pp. 1–6, Jun. 2021. doi: 10.14303/irjps.2021.16. [Online]. Available: https://www.interesjournals.org/plant-science.html

S. Aravind, S. Harini, and V. K. A., “Cassava leaf disease classification using deep learning,” Natural Volatiles & Essential Oils, vol. 8, no. 5, pp. 9375–9389, 2021.

J. Sunanthini and J. J. Charles Lifrin Packiyam, “ML Based Farming System – Cassava Leaf Disease Detection,” International Journal of Science, Engineering and Technology, vol. 11, no. 2, 2023, ISSN (Online): 2348-4098, ISSN (Print): 2395-4752.

Y. Shendye, “Cassava Plant Leaf Disease Detection,” International Journal of Science and Research, ISSN: 2319-7064, SJIF (2020): 7.803.

O. O. Abayomi‐Alli, R. Damaševičius, S. Misra, and R. Maskeliūnas, “Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning,” Expert Systems, vol. 38, no. 4, 2021. doi: 10.1111/exsy.12746. [Online]. Available: https://doi.org/10.1111/exsy.12746

A. M. Abdu, M. M. M. Mokji, and U. U. U. Sheikh, “Machine learning for plant disease detection: An investigative comparison between support vector machine and deep learning,” IAES International Journal of Artificial Intelligence, vol. 9, no. 4, pp. 670–683, 2020. doi: 10.11591/ijai.v9.i4.pp670-683. [Online]. Available: https://doi.org/10.11591/ijai.v9.i4.pp670-683




DOI: http://dx.doi.org/10.52155/ijpsat.v52.1.7405

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Chika Norah John

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.