Kennedy O. Okokpujie; Osondu C. Ronald; Joshua S. Mommoh; Mary O. Ogundele; OluwadamiI Oguntuyo

Development of a Real-Time Maize Leaf Disease Classification System Deployed on Web and Mobile-Based Applications

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Author(s)

Kennedy O. Okokpujie ^1,* Osondu C. Ronald ¹ Joshua S. Mommoh ² Mary O. Ogundele ³ OluwadamiI Oguntuyo ⁴

1. Department of Electrical and Information Engineering, Covenant University, Ota, 112221, Nigeria

2. Department of Computer Engineering, Veritas University Abuja, Abuja, FCT, 901101, Nigeria

3. Department of Network and User Administration, NigeriaUnified Payment Idowu Martins, Victoria Island, Lagos, Nigeria

4. Department of Mathematics, College of the Holy Cross, Worcester, Massachusetts, United States

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2026.04.02

Received: 28 May 2026 / Revised: 15 Jun. 2026 / Accepted: 15 Jul. 2026 / Published: 8 Aug. 2026

Index Terms

Maize Disease, DCNN, Transfer Learning, MobileNetV2, Web application, Mobile applications, API

Abstract

Agriculture remains at the core of human life, providing staple food and livelihood for millions worldwide. Among its different domains, food crops directly enter the human system, while cash crops are grown primarily for monetary gains. Maize, as one of the most extensively grown and consumed food crops, is of gigantic economic and nutritional value, particularly in West Africa. Unfortunately, maize plant diseases have adversely impacted farmer yields, resulting in decreased maize production. This research aims to create a system that can identify diseases in maize based on images of the leaves. Three deep convolutional neural network (DCNN) models, namely MobileNetV2, InceptionV3, and ResNet50, were selected to achieve this goal because of their prior ability. The transfer learning technique was adopted to develop new models for classifying maize disease using a hybrid maize leaf image dataset comprising 6,543 images from the University of Pretoria and Kaggle repositories. Furthermore, the dataset was split into 80% for training, 10% for validation, and 10% for testing and the three model were configured and trained. According to the evaluation results, MobileNetV2 was the best model for classifying maize leaf diseases, with a 95.29% classification accuracy. In comparison, InceptionV3 and ResNet-50 yielded accuracies of 92.18% and 74.48%, respectively. The MobileNetV2 was chosen for the dual deployment in both a web-based and a mobile application due to its exceptional performance metrics and its lightweight API. Evaluation of the deployed MobileNetV2 model on both the web and mobile applications showed that it achieved an average confidence rate of 91% on both platforms, with response times of 0.159 s and 0.163 s, and throughputs of 5.88 images/s and 6.28 images/s, respectively. This research offers a simple and intuitive tool for users and agricultural professionals to quickly detect maize leaf diseases and take necessary precautions to mitigate losses.

Cite This Paper

Kennedy O. Okokpujie, Osondu C. Ronald, Joshua S. Mommoh, Mary O. Ogundele, OluwadamiI Oguntuyo, "Development of a Real-Time Maize Leaf Disease Classification System Deployed on Web and Mobile-Based Applications", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.4, pp.19-39, 2026. DOI:10.5815/ijem.2026.04.02

Reference

[1]Eason, G., Macauley, H., & Ramadjita, T. (2015). Feeding Africa: Cereal Crops: Rice, Maize, Millet, Sorghum, Wheat. Proceedings of the Feeding Africa Conference, Abdou Diouf International Conference Centre, Senegal, Vol. 86, p. 74.
[2]Wossen, T., Menkir, A., Alene, A., Abdoulaye, T., Ajala, S., Badu-Apraku, B., Gedil, M., Mengesha, W., & Meseka, S. (2023). Drivers of transformation of the maize sector in Nigeria. Global Food Security, 38, 100713. DOI: 10.1016/j.gfs.2023.100713
[3]Norgrove, L. (2021). Trade-offs in maize seedling losses in African grasslands. Crop Protection, 146, 105676. DOI: 10.1016/j.cropro.2021.105676
[4]Mommoh, J. S., Obetta, J. L., John, S. N., Okokpujie, K., Omoruyi, O. N., & Awelewa, A. A. (2024). Detection of fruit ripeness and defectiveness using convolutional neural networks. Information Dynamics and Applications, 3(3), 184–199. DOI: 10.56578/ida030304
[5]Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine learning applications in agriculture: Current trends, challenges, and future perspectives. Agronomy, 13(12), 2976. DOI: 10.3390/agronomy1312297
[6]John, S. N., Musa, N. A., Mommoh, J. S., Noma-Osaghe, E., Udioko, U. I., & Obetta, J. L. (2025). Development of a machine learning-driven web platform for automated identification of rice insect pests. Acadlore Transactions on AI and Machine Learning, 4(3), 137–156. DOI: 10.56578/ataiml040301
[7]Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57, 99. DOI: 10.1007/s10462-024-10721-6
[8]Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134. DOI: 10.1016/j.mlwa.2021.100134
[9]Paul, H., Udayangani, H., Umesha, K., Lankasena, N., Liyanage, C., & Thambugala, K. (2023). Maize leaf disease detection using convolutional neural network: A mobile application based on pre-trained VGG16 architecture. New Zealand Journal of Crop and Horticultural Science, 1–17. DOI: 10.1080/01140671.2024.2385813
[10]Bachhal, P., Kukreja, V., Ahuja, S., Lilhore, U. K., Simaiya, S., Bijalwan, A., Alroobaea, R., & Algarni, S. (2024). Maize leaf disease recognition using PRF-SVM integration: A breakthrough technique. Scientific Reports, 14, 10219. DOI: 10.1038/s41598-024-60506-8
[11]Khan, I., Sohail, S. S., Madsen, D. Ø., & Khare, B. K. (2024). Deep transfer learning for fine-grained maize leaf disease classification. Journal of Agriculture and Food Research, 16, 101148. DOI: 10.1016/j.jafr.2024.101148
[12]Khan, F., Zafar, N., Tahir, M. N., Aqib, M., Waheed, H., & Haroon, Z. (2023). A mobile-based system for maize plant leaf disease detection and classification using deep learning. Frontiers in Plant Science, 14. DOI: 10.3389/fpls.2023.1079366
[13]Ma, Z., Wang, Y., Zhang, T., Wang, H., Jia, Y., Gao, R., & Su, Z. (2022). Maize leaf disease identification using deep transfer convolutional neural networks. International Journal of Agricultural and Biological Engineering, 15(5).
[14]Bachhal, P., Kukreja, V., & Ahuja, S. (2023). Maize leaf diseases classification using a deep learning algorithm. Proceedings of the 4th International Conference for Emerging Technology (INCET), Belgaum, India, 1–5. DOI: 10.1109/INCET57972.2023.10170182
[15]Dash, A., Sethy, P. K., & Behera, S. K. (2023). Maize disease identification based on optimized support vector machine using deep feature of DenseNet201. Journal of Agriculture and Food Research, 14. DOI: 10.1016/j.jafr.2023.100824
[16]Prasannavenkatesan, P. T., Ruby, A. U., Chandran, J. G. C., Sardar, T. H., & Shafeeq, A. S. B. M. (2024). Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases. Journal of Big Data, 11, 112. DOI: 10.1186/s40537-024-00972-z
[17]Zhu, T., Yan, F., Lv, X., Zhao, H., Wang, Z., Dong, K., Fu, Z., Jia, R., & Lv, C. (2024). A deep learning model for accurate maize disease detection based on state-space attention and feature fusion. Plants, 13, 3151. DOI: 10.3390/plants13223151
[18]Çakmak, M. (2024). Automatic maize leaf disease recognition using deep learning. SAUCIS, 7(1), 61–76. DOI: 10.35377/saucis.1418505
[19]Craze, H. (2022). Diseases of maize in the field. University of Pretoria Dataset. DOI: 10.25403/UPresearchdata.20237613.v1
[20]Mamillapalli, S. (2023). Corn or Maize Leaf Disease Dataset. Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/sahityamamillapalli/corn-or-maize-leaf-disease-dataset. [Accessed: 04-Jan-2024].
[21]Riyadi, S., Abidin, F. A., & Audita, N. (2024). Comparison of ResNet50V2 and MobileNetV2 models in building architectural style classification. Proceedings of the International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 1–8. DOI: 10.1109/ISCV60512.2024.10620099
[22]Setiawan, W., Suhadi, M. M., Pramudita, Y. D., & Mulaab. (2024). Inception-v3 with reduce learning rate for optimization of lung cancer histopathology classification. Ingénierie des Systèmes d’Information, 29(2), 561–570.
[23]Neamah, K., Mohamed, F., Waheed, S. R., Kurdi, W. H. M., Taha, A. Y., & Kadhim, K. A. (2024). Utilizing deep improved ResNet50 for brain tumor classification based MRI. IEEE Open Journal of the Computer Society, 5, 446–456. DOI: 10.1109/OJCS.2024.3453924
[24]Okokpujie, K., Noma-Osaghae, E., John, S. N., Ndujiuba, C., & Okokpujie, I. P. (2021). Comparative analysis of augmented datasets performances of age invariant face recognition models. Bulletin of Electrical Engineering and Informatics, 10(3), 1356–1367. DOI: 10.11591/eei.v10i3.3020
[25]Mommoh, J. S., Noma-Osaghe, E., Timai, V. N., Uchechi, A. O., Ibharunujele, S. O., & Godday, B. (2025). Development of an artificial intelligence web based system for classification of tomatoes disease. Mansoura Engineering Journal, 50(6), Article 15. DOI: 10.58491/2735-4202.3344
[26]Okokpujie, K., John, S., Ndujiuba, C., Badejo, J. A., & Noma-Osaghae, E. (2021). An improved age invariant face recognition using data augmentation. Bulletin of Electrical Engineering and Informatics, 10(1), 179–191. DOI: 10.11591/eei.v10i1.2356

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