Maize Disease Detection: Multi-Format Image Analysis Using Deep Learning for Precise Diagnosis
DOI:
https://doi.org/10.22105/scfa.v2i1.39Keywords:
Maize disease detection, Deep learning, Multi-format analysis, Ensemble learning, Plant pathologyAbstract
This research introduces a comprehensive deep learning strategy designed for identifying maize diseases, utilizing RGB, grayscale, and segmented images to enhance classification precision and dependability. By utilizing Convolutional Neural Networks (CNNs), the model was trained on a dataset featuring prominent maize diseases, such as Cercospora leaf spot, Common rust, Northern leaf blight, along with healthy maize foliage. The study implements a multi-format ensemble approach that takes advantage of a majority voting system to merge predictions from all image formats, resulting in a remarkable classification accuracy of 94.3%. This approach surpasses models based on single formats and offers a scalable, instantaneous solution for the early detection of maize diseases. The integration of image processing, feature extraction, and deep learning guarantees strong performance across various disease types, making it a valuable resource for agricultural practices and early intervention. The results emphasize the potential to improve crop management strategies, especially in areas where prompt disease detection is vital for preserving crop yield and quality.
References
[1] Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K., & Prasanna, B. M. (2022). Global maize production, consumption and trade: trends and R&D implications. Food security, 14(5), 1295–1319. https://doi.org/10.1007/s12571-022-01288-7
[2] Dhugga, K. S. (2007). Maize biomass yield and composition for biofuels. Crop science, 47(6), 2211–2227. https://doi.org/10.2135/cropsci2007.05.0299
[3] Ostrander, B. (2015). Maize starch for industrial applications. In industrial crops. handbook of plant breeding ( pp. 171–189). Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1447-0_9
[4] Martin, D. P., & Shepherd, D. N. (2009). The epidemiology, economic impact and control of maize streak disease. Food security, 1, 305–315. https://doi.org/10.1007/s12571-009-0023-1
[5] Logrieco, A., Battilani, P., Leggieri, M. C., Jiang, Y., Haesaert, G., Lanubile, A., … Pasti, M. (2021). Perspectives on global mycotoxin issues and management from the MycoKey maize working group. Plant disease, 105(3), 525–537. https://doi.org/10.1094/PDIS-06-20-1322-FE
[6] Mesterházy, Á., Lemmens, M., & Reid, L. M. (2012). Breeding for resistance to ear rots caused by Fusarium spp. in maize–a review. Plant breeding, 131(1), 1–19. https://doi.org/10.1111/j.1439-0523.2011.01936.x
[7] Carvajal-Moreno, M. (2022). Mycotoxin challenges in maize production and possible control methods in the 21st century. Journal of cereal science, 103, 103293. https://doi.org/10.1016/j.jcs.2021.103293
[8] Fatma, H. K., Tileye, F., & Patrick, A. N. (2016). Insights of maize lethal necrotic disease: A major constraint to maize production in East Africa. African journal of microbiology research, 10(9), 271–279. https://doi.org/10.5897/AJMR2015.7534
[9] Ekwomadu, T. I., & Mwanza, M. (2023). Fusarium fungi pathogens, identification, adverse effects, disease management, and global food security: A review of the latest research. Agriculture, 13(9), 1810. https://doi.org/10.3390/agriculture13091810
[10] Singh, V. K., Singh, R., Kumar, A., & Bhadouria, R. (2021). Current status of plant diseases and food security. In food security and plant disease management (pp. 19–35). Elsevier. https://doi.org/10.1016/B978-0-12-821843-3.00019-2
[11] Kalunga, P., & Kunda, D. (2024). Investigation of the suitability of existing maize plant leaf disease detection and classification approaches: challenges and open issues. Zambia ict journal, 8(1), 70–79. https://doi.org/10.33260/zictjournal.v8i1.343
[12] Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023). A review of deep learning techniques used in agriculture. Ecological informatics, 77, 102217. https://doi.org/10.1016/j.ecoinf.2023.102217
[13] Bhise, D., Kumar, S., & Mohapatra, H. (2022). Review on deep learning-based plant disease detection. 2022 6th international conference on electronics, communication and aerospace technology (pp. 1106–1111). IEEE. https://doi.org/10.1109/ICECA55336.2022.10009290
[14] Mishra, S. R., Mohapatra, H., & Saxena, S. (2024). Leveraging data analytics and a deep learning framework for advancements in image super-resolution techniques: from classic interpolation to cutting-edge approaches. In data analytics and machine learning: navigating the big data landscape (pp. 105–126). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0448-4_6