Machine Learning-Driven Detection of Corn Leaf Diseases for Smart Agriculture
DOI:
https://doi.org/10.54386/jam.v28i2.3367Keywords:
Corn leaf, Disease prediction, Machine learning, Ensemble approachAbstract
In this study, the authors attempted to predict corn disease using machine learning (ML) algorithms. They attempted to predict the crop disease into four categories, such as healthy (class 1), Grey Leaf Spot (class 2), Common Rust (class 3), and Northern Leaf Blight (class 4), using bagging, boosting, random forest and ensemble algorithms. The entire database is split into a 70:30 ratio for training and testing the classifiers, respectively, and a 5-fold cross-validation has been done to evaluate the performance of the classifier. They used a handcrafted feature extraction method to extract the features from the leaf image, such as color, texture, vegetation indices, and morphological features and fed them into the machine learning algorithms for further classification. The ensemble learning technique combines different ML supervised algorithms and predicts the result by majority voting. The usage of the ensemble technique may overcome the different types of errors and focus on different data patterns as multiple ML techniques are used. The overall accuracy of Bagging, boosting, random forest, and ensemble algorithms is 84.6%, 86.9%, 89.6%, and 91.9%, respectively. Compared to the other methods, the ensemble algorithm exhibits more accuracy. The class-wise healthy, Grey Leaf Spot, Common Rust, and Northern Leaf Blight accuracy is 99.1%, 97.5%, 98.3%, and 98.3%, respectively, for the ensemble model. Though the ensemble techniques combine 3 different types of ML algorithms for prediction, the average time taken to predict the disease is about 6.89 ms. Thus, the authors suggest that the ensemble algorithm predicts crop disease better than individual ML techniques.
References
American Phytopathological Society. (2020). Gray leaf spot of corn. APS Education Center. https://doi.org/10.1094/PHI-I-2000-0726-01
American Phytopathological Society. (2021). Northern leaf blight of corn. APS Education Center. https://doi.org/10.1094/PHI-I-2000-0727-01
Bi, Z., Ma, F., Guan, J., Wu, J., Li, J., Li, F., & Li, Y. (2025). Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm. Scientific Reports, 15, 30071. https://doi.org/10.1038/s41598-025-15246-8
Çakmak, M. (2024). Automatic maize leaf disease recognition using deep learning. IET Image Processing, 18(7), 61–76. https://doi.org/10.1049/ipr2.12945
CIMMYT. (2004). Maize diseases: A guide for field identification. International Maize and Wheat Improvement Center.
Dawood, K. A., Gadalla, O. A. A., Oztekin, Y. B., & Baitu, G. P. (2024). Machine learning-based automation detection of corn plant disease using image processing. Journal of Agricultural Sciences, 30(3), 464–476. https://doi.org/10.15832/ankutbd.1288298
Enow, T. A. A., Ngalle, H. B., & Ngonkeu, M. E. L. (2025). Automated estimation of plant leaf disease severity using classical image segmentation techniques. Biotechnology Journal International, 29(1), 59–76. https://doi.org/10.9734/bji/2025/v29i1597
Fadhilla, M., Suryani, D., Labellapansa, A., & Gunawan, H. (2023). Corn leaf diseases recognition based on convolutional neural network. International Journal of Intelligent Technology Research and Development. https://doi.org/10.25299/itjrd.2023.13904
FAO. (2018). Integrated management of maize diseases. Food and Agriculture Organization of the United Nations.
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009
Hazra, D., Bhattacharyya, D., & Kin, T. H. (2021). A random forest based classification using multiple features. Advances in Intelligent Systems and Computing, 2, 227–239. https://doi.org/10.1007/978-981-16-8364-0
Hooker, A. L. (1985). Corn and sorghum rust. In A. P. Roelfs & W. R. Bushnell (Eds.), The cereal rusts (Vol. 2, pp. 211–237). University of Minnesota Press.
Hughes, D. P., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv. http://arxiv.org/abs/1511.08060
Javidan, S. M., Banakar, A., Rahnama, K., Vakilian, K. A., & Ampatzidis, Y. (2024). Feature engineering to identify plant diseases using image features including morphology and lesion metrics. Smart Agricultural Technology, 8, 100433. https://doi.org/10.1016/j.atech.2024.100433
Khandagale, H. P., Patil, S., Gavali, V. S., Chavan, S. V., Halkarnikar, P. P., & Meshram, P. A. (2025). Design and implementation of FourCropNet: A CNN-based system for efficient multi-crop disease detection and management. Journal of Information Systems Engineering and Management, 10(1), 461–471. https://doi.org/10.52783/jisem.v10i1.461
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
Mueller, D. S., Wise, K. A., Sisson, A. J., Allen, T. W., Bergstrom, G. C., Bosley, D. B., Bradley, C. A., Broders, K. D., Byamukama, E., Chilvers, M. I., Collins, A., Faske, T. R., Friskop, A. J., Heiniger, R. W., Hollier, C. A., Hooker, D. C., Isakeit, T., Jackson-Ziems, T. A., Jardine, D. J., & Warner, F. (2016). Corn yield loss estimates due to diseases in the United States and Ontario, Canada from 2012 to 2015. Plant Health Progress, 17(3), 211–222.
Pataky, J. K., Raid, R. N., & du Toit, L. J. (2009). Rust diseases of maize. APS Education Center. https://doi.org/10.1094/PHI-I-2009-0518-01
Paul, S., Das, S., Khan, M. R., Srivastava, A., Nabapure, S., Roy, A., & Sinha, P. (2026). YOLOv9t-DyE: A lightweight detection framework with SAM-assisted segmentation for quantifying chilli leaf curl complex. Smart Agricultural Technology, 13, 101764. https://doi.org/10.1016/j.atech.2025.101764
Paul, S., Emmadi, V., Sarkar, M., Das, S., Roy, A., & Sinha, P. (2025). SCA-MobiPlant: Smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex. Plant Methods, 21, 138. https://doi.org/10.1186/s13007-025-01453-x
Rossai, R. L. de, Guerra, F. A., Plazas, M. C., Vuletic, E. E., Brucher, E., Guerra, G. D., & Reis, E. M. (2022). Crop damage, economic losses, and the economic damage threshold for northern corn leaf blight. Crop Protection, 154, 105891. https://doi.org/10.1016/j.cropro.2022.105891
Thirumala Lakshmi, K., & Usha Kingsly Devi, K. (2020). Semantic classification of images in hierarchical manner using fuzzy rules and HSVM classifier. Journal of Image Processing and Pattern Recognition, 7, 33–54.
Timilsina, S., Sharma, S., & Konda, S. (2025). Advancements in maize leaf disease detection, segmentation and classification: A review. Biosystems Engineering, 255, 1–31. https://doi.org/10.1016/j.biosystemseng.2025.01.001
Vimalkumar, S., & Latha, R. (2024). Maize leaf disease detection using Manta-Ray Foraging Optimization with deep learning model. Engineering, Technology & Applied Science Research, 14, 17068–17074. https://doi.org/10.48084/etasr.6830
Ward, J. M. J., Stromberg, E. L., Nowell, D. C., & Nutter, F. W. (1999). Gray leaf spot: A disease of global importance in maize production. Plant Disease, 83(10), 884–895. https://doi.org/10.1094/PDIS.1999.83.10.884
Yang, S., Yao, J., & Teng, G. (2024). Corn leaf spot disease recognition based on improved YOLOv8. Agriculture, 14(5), 666. https://doi.org/10.3390/agriculture14050666
Downloads
Published
How to Cite
License
Copyright (c) 2026 K. THIRUMALA LAKSHMI, K. THENDRAL, V. SUDHA, M. SIVA
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is a human-readable summary of (and not a substitute for) the license. Disclaimer.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
