Detection of Cassava Plant Disease using Deep Transfer Learning Approach
Abstract
Small-scale farmers use cassava as an essential crop for food and nutrition security, owing to its capacity to flourish under adverse situations. In numerous African countries, it serves as a significant source of carbohydrates. Leaf diseases can sometimes damage cassava crops, constraining overall output and farmers' revenue. The ongoing research on cassava disease is fraught with challenges, including a low detection rate, extended processing time, and inadequate precision. This research employs deep transfer learning with Visual Geometry Group (VGG16) models for the diagnosis of Cassava leaf diseases. An experimental study is conducted on a dataset of 5,656 images of cassava, categorized into four distinct disease classifications. Two of the most sophisticated predictions were generated: one identified the healthy leaf, while the other detected the diseases present on the unhealthy leaf. Our suggested deep transfer learning model attains a promising accuracy of 88% and an F1-score of 82% on the public plant disease dataset from the Kaggle repository, achieved through effective hyperparameter fine-tuning. The results of this study strongly advocate for additional research and practical application of deep learning models in plant disease diagnostics.
Downloads
References
W. G. Adebayo, “Cassava production in africa: A panel analysis of the drivers and trends,” Heliyon, vol. 9, no. 9, 2023.
M. N. Kiboi, F. Ngetich, M. Mucheru-Muna, J. Diels, and D. Mugendi, “Soil nutrients and crop yield response to conservation-effective management practices in the sub-humid highlands agro-ecologies of kenya,” Heliyon, vol. 7, no. 6, 2021.
S. Tatineni and G. L. Hein, “Plant viruses of agricultural importance: Current and future perspectives of virus disease management strategies,” Phytopathology®, vol. 113, no. 2, pp. 117–141, 2023.
G. A. Taiwo, A. Alameer, and T. Mansouri, “Review of farmer-centered ai systems technologies in livestock operations,” CABI Reviews, vol. 19, no. 1, 2024.
J. P. Legg, P. L. Kumar, T. Makeshkumar, L. Tripathi, M. Ferguson, E. Kanju, P. Ntawuruhunga, and W. Cuellar, “Cassava virus diseases: biology, epidemiology, and management,” in Advances in virus research, vol. 91, pp. 85–142, Elsevier, 2015.
S. R. Dubey and A. S. Jalal, “Adapted approach for fruit disease identification using images,” International journal of computer vision and image processing (IJCVIP), vol. 2, no. 3, pp. 44–58, 2012.
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in plant science, vol. 7, p. 215232, 2016.
W. Abdullakasım, K. Powbunthorn, J. Unartngam, and T. Takıgawa, “An images analysis technique for recognition of brown leaf spot disease in cassava,” Tarım Makinaları Bilimi Dergisi, vol. 7, no. 2, pp. 165–169, 2011.
S. Akinpelu and S. Viriri, “A robust deep transfer learning model for accurate speech emotion classification,” in International Symposium on Visual Computing, pp. 419–430, Springer, 2022.
C. Y. Daramola, S. A. Akinpelu, E. O. Akinyemi, S. A. Ajagbe, G. A. Akinpelu, and M. O. Adigun, “Malicious query recognition using chosen machine learning techniques,” SN Computer Science, vol. 6, no. 3, p. 281, 2025.
E. Moses, J. Asafu-Agyei, K. Adubofour, and A. Adusei, “Guide to identification and control of cassava diseases,” CSIR-Crop Research Institute, Kumasi, Ghana, 2008.
P. C. Chikoti, R. M. Mulenga, M. Tembo, and P. Sseruwagi, “Cassava mosaic disease: a review of a threat to cassava production in zambia,” Journal of Plant Pathology, vol. 101, no. 3, pp. 467–477, 2019.
A. K. Gautam and S. Kumar, “Techniques for the detection, identification, and diagnosis of agricultural pathogens and diseases,” in Natural remedies for pest, disease and weed control, pp. 135–142, Elsevier, 2020.
A. Maredza, F. Allie, G. Plata, and M. Rey, “Sequences enhancing cassava mosaic disease symptoms occur in the cassava genome and are associated with south african cassava mosaic virus infection,” Molecular Genetics and Genomics, vol. 291, pp. 1467–1485, 2016.
M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, “A survey on the role of iot in agriculture for the implementation of smart farming,” Ieee Access, vol. 7, pp. 156237–156271, 2019.
A. Darwish, A. E. Hassanien, and S. Das, “A survey of swarm and evolutionary computing approaches for deep learning,” Artificial intelligence review, vol. 53, no. 3, pp. 1767–1812, 2020.
A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep learning for image-based cassava disease detection,” Frontiers in plant science, vol. 8, p. 1852, 2017.
I. Sangbamrung, P. Praneetpholkrang, and S. Kanjanawattana, “A novel automatic method for cassava disease classification using deep learning,” Journal of Advances in Information Technology vol. 11, no. 4, pp. 241–248, 2020.
G. Sambasivam and G. D. Opiyo, “A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks,” Egyptian informatics journal, vol. 22, no. 1, pp. 27–34, 2021.
S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, and D. Traore, “Deep neural networks with transfer learning in millet crop images,” Computers in industry, vol. 108, pp. 115–120, 2019.
J. Lu, J. Hu, G. Zhao, F. Mei, and C. Zhang, “An in-field automatic wheat disease diagnosis system,” Computers and electronics in agriculture, vol. 142, pp. 369–379, 2017.
K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and electronics in agriculture, vol. 145, pp. 311–318, 2018.
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. 7, p. e12746, 2021.
G. Capizzi, G. Lo Sciuto, C. Napoli, E. Tramontana, M. Woźniak, et al., “A novel neural networks-based texture image processing algorithm for orange defects classification,” International journal of advanced computer science and applications, vol. 13, no. 2, pp. 45–60, 2016.
J. Zhang, B. Zhang, C. Qi, I. Nyalala, P. Mecha, K. Chen, and J. Gao, “Maianet: Signal modulation in cassava leaf disease classification,” Computers and Electronics in Agriculture, vol. 225, p. 109351, 2024.
J. Srivastava et al., “Cassava leaf disease detection using deep learning,” in 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–7, IEEE, 2022.
E. Mwebaze, T. Gebru, A. Frome, S. Nsumba, and J. Tusubira, “icassava 2019 fine-grained visual categorization challenge,” arXiv preprint arXiv:1908.02900, 2019.
H. Ayu, A. Surtono, and D. Apriyanto, “Deep learning for detection cassava leaf disease,” in Journal of Physics: Conference Series, vol. 1751, p. 012072, IOP Publishing, 2021.
R. Surya and E. Gautama, “Cassava leaf disease detection using convolutional neural networks,” in 2020 6th international conference on science in information technology (ICSITech), pp. 97–102, IEEE, 2020.
Copyright (c) 2025 ParadigmPlus
This work is licensed under a Creative Commons Attribution 4.0 International License.
