International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 16, No. 2, 8 Apr. 2026
Cover page and Table of Contents: PDF (size: 641KB)
A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
PDF (641KB), PP.185-195
Views: 0 Downloads: 0
Author(s)
Paul Kehinde Olotu
1
Temitayo Elijah Balogun
2,*
Suliyat Temitope Ajadi
1
Sakirat Adenike Olubi
3
Index Terms
Maize Disease Detection, ResNet50, EfficientNet, AI deployment
Abstract
Maize is a cornerstone of food security and economic stability in Nigeria, yet its production is severely hampered by crop diseases that cause significant yield losses and threaten the livelihoods of millions of smallholder farmers. Despite advances in machine learning (ML) and deep learning (DL) for plant disease detection, existing solutions often lack generalizability, scalability, and accessibility for resource-limited settings. This research used a robust, predictive system that leverages convolutional neural networks, specifically ResNet50 and EfficientNet trained on diverse, annotated datasets of maize leaf images. By integrating computer vision, transfer learning, and user-centric mobile application design, the system aimed to provide real-time, accurate diagnosis and actionable recommendations for disease management. This study compared the performance of the ResNet50 and the EfficientNet. At the end of the research, ResNet50 achieved marginally higher accuracy than EfficientNet under the same experimental conditions, although the performance difference is small and not statistically tested. The ResNet50 model was thereafter deployed into a scalable mobile application tool that can empower farmers and extension workers with early disease detection capabilities, potentially reducing crop losses, improving productivity, and enhancing food security across sub-Saharan Africa.
Cite This Paper
Paul Kehinde Olotu, Temitayo Elijah Balogun, Suliyat Temitope Ajadi, Sakirat Adenike Olubi, "A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.2, pp.185-195, 2026. DOI:10.5815/ijem.2026.02.12
Reference
[1]USDA (2025) Grain and Feed Annual. United States Department of Agriculture. Available at: https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName?fileName=Grain%20and%20Feed%20Annual_Lagos_Nigeria_NI2025-0003.pdf#:~:text=FAS%2DLagos%20forecasts%20MY%202024/25%20production%20at%2012,the%20cost%20of%20inputs%2C%20fuel%2C%20and%20logistics. [Accessed: 12 May 2025]
[2]PwC (2021) Positioning Nigeria as Africa's leader in maize production for AfCFTA. PricewaterhouseCoopers. Available at https://www.pwc.com/ng/en/assets/pdf/positioning-nigeria-as-africa-leader-in-maize-production-for-afcfta.pdf. [Accessed: 10 April 2025]
[3]Peddicord, L., Xavier, A., Cryer, S., Barr, J., & Heijden, G. V. D. (2025). Scalable Prediction of Northern Corn Leaf Blight and Gray Leaf Spot Diseases to Predict Fungicide Spray Timing in Corn. Agronomy, 15(2), 328.
[4]Pfordt, A., & Paulus, S. (2025). A review on detection and differentiation of maize diseases and pests by imaging sensors. Journal of Plant Diseases and Protection, 132(1), 1-21.
[5]Biswal, A. K., Alakonya, A. E., Mottaleb, K. A., Hearne, S. J., Sonder, K., Molnar, T. L., ... & Prasanna, B. M. (2022). Maize Lethal Necrosis disease: review of molecular and genetic resistance mechanisms, socio-economic impacts, and mitigation strategies in sub-Saharan Africa. BMC Plant Biology, 22(1), 542.
[6]Boddupalli, P., Suresh, L. M., Mwatuni, F., Beyene, Y., Makumbi, D., Gowda, M., ... & Lassen, P. (2020). Maize lethal necrosis (MLN): Efforts toward containing the spread and impact of a devastating transboundary disease in sub-Saharan Africa. Virus Research, 282, 197943
[7]Karanja, T., Gatua, E., & Oronje, M. (2012). Maize lethal necrosis disease. PlantwisePlus Knowledge Bank, Factsheets for Farmers, 20137804184. https://doi.org/10.1079/pwkb.20137804184
[8]Mapiye, O., Makombe, G., Molotsi, A., Dzama, K., & Mapiye, C. (2023). Information and communication technologies (ICTs): The potential for enhancing the dissemination of agricultural information and services to smallholder farmers in sub-Saharan Africa. Information Development, 39(3), 638-658
[9]Kansiime, M. K., Mugambi, I., Rware, H., Alokit, C., Aliamo, C., Zhang, F., ... & Romney, D. (2022). Challenges and capacity gaps in smallholder access to digital extension and advisory services in Kenya and Uganda. Frontiers of Agricultural Science and Engineering, 9(4), 642-654
[10]Balogun, T. E., Osasona, A. V., & Bamisaye, V. J. (2024). Enhancing agricultural information dissemination: an empirical analysis of the influence of information and communication technology on agricultural producers in Ekiti State. International Journal of Agriculture Innovation, Technology and Globalisation, 4(1), 20-36
[11]Balogun, T., Saliu, R., Faluyi, S., & Fapohunda, K. (2022). Comparative analysis of deep learning models for the detection and classification of Diabetes Retinopathy. In 2022 5th Information Technology for Education and Development (ITED) (pp. 1-6). IEEE.
[12]Balogun, T. E, Akinyede R. O., & Faluyi, S. G. (2024) Comparative analysis of convolutional neural networks and rule-based techniques for epileptic seizure detection from electroencephalograph signals using a text classification approach. In The 7th International Conference on Informatics & Data-Driven Medicine (IDDM 2024). (pp.1-10, ISSN:1613-0073. https://ceur-ws.org/) Birmingham, United Kingdom. November 14th – 16th, 2024
[13]Ullah, K., Jan, M. A., & Sayyed, A. (2021). Automatic diseases detection and classification in maize crop using convolution neural network. International Journal, 10(2).
[14]Ashwini, C., & Sellam, V. (2022). Corn disease detection based on deep neural network for substantiating the crop yield. Appl Math, 16(3), 423-433.
[15]Rahman, S., Vhaduri, S., & Faezipour, M. (2022, December). Automatic Plant Disease Detection Using Deep Learning. In 2022 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 561-564). IEEE
[16]Haris M, Tripathi A, Pandey S S, Tiwari H, Ansari AA (2025). A Deep Learning Based Model for Plant Disease Detection. Journal of Neonatal Surgery. 14(15s) (pp. 913-925)
[17]Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., ... & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14, 1158933
[18]Priyanka, B. (2024). Machine Learning Driven Precision Agriculture: Enhancing Farm Management through Predictive Insights. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 195–201. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6705 (Accessed: 7 June 2025).
[19]Metagar, S. M., & Walikar, G. A. (2024). Machine learning Models for Plant disease Prediction and Detection: A review. Agricultural Science Digest, 44(4), 591-602
[20]Polwaththa, K. P. G. D. M., Amarasinghe, S. T. C., Amarasinghe, A. A. Y. D., & Amarasinghe, A. A. Y. (2024). Exploring Artificial Intelligence and Machine Learning in Precision Agriculture: A Pathway to Improved Efficiency and Economic Outcomes in Crop Production. American Journal of Agricultural Science, Engineering, and Technology, 8(3), 50-59
[21]Ajithkumar, S., Jayanthi, M., Priyadharshini, P., Divya, M. S., Parveen, M. F., & Gayathri, A. (2025, May). Plant Disease Detection using Machine Language. In International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024) (pp. 1309-1319). Atlantis Press
[22]Geetharamani, G. A. P. J., & Pandian, A. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76, 323-338.