APPLICATION OF DEEP LEARNING TECHNOLOGY TO CREATE A WEB APPLICATION FOR CORN LEAF DISEASE ANALYSIS
Keywords:
Corn Diseases, Image Processing, YOLO, Deep Learning, Web ApplicationAbstract
This research aims to develop a web application for analyzing corn leaf diseases by applying deep learning techniques based on object detection to identify disease types from corn leaf images. The proposed system employs the YOLOv11 architecture to detect and classify three types of corn leaf diseases: corn common rust, downy mildew, and corn leaf blight. The dataset used in this study consists of 450 corn leaf images, which were divided into training and testing sets at a ratio of 50:50. Due to the limited size of the dataset, the focus is on practical application rather than competing on accuracy, in order to reduce the problem of overfitting. The system performance was evaluated using standard metrics, including accuracy, precision, recall, and F1-score.
Experimental results demonstrate that the proposed model can effectively detect and classify corn leaf diseases with satisfactory accuracy and can be deployed in real-time through a web application. The findings indicate the potential of deep learning-based object detection techniques to support plant disease analysis and contribute to the advancement of precision agriculture in the future.
References
Ahmad A., Saraswat D. & Gamal E A. (2022). A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Computers an Electronics in Agriculture.https://www.sciencedirect.com/science/article/pii/S277237552200048X
Aldakheel, E. A., Zakariah, M., & Alabdalall, A. H. (2024). Detection and identification of plant leaf diseases using YOLOv4. Frontiers in Plant Science, 15, 1355941. doi: 10.3389/fpls.2024.1355941
Alhwaiti, Y., Khan, M., Asim, M., Siddiqi, M. H., Ishaq, M., & Alruwaili, M. (2025). Leveraging YOLO deep learning models to enhance plant disease identification.Scientific Reports, 15, 7969. doi: 10.1038/s41598-025-92143-0
Andrew, J., Eunice J., Popescu D., Chowdary, M., & Hemanth, J. (2022). Deep Learning-Based Leaf Disease Detection in Crops Using Pre-trained Models. Agronomy. 12, 2395.https://www.mdpi.com/2073-4395/12/10/2395
Askale, G.T., Yibel, A.B., Taye, B.M., &Wubneh, G.D. (2025). Mobile based deep CNN model for maize leaf disease detection and classification. Plant Methods, 21(72). https://doi.org/10.1186/s13007-025-01386-5
Cap, Q. H., Uga, H., Kagiwada, S., & Iyatomi, H. (2020). LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis. arXiv. doi: 10.48550/arXiv.2002.10100
Fraiwan, M., Esraa, F., and Khasawneh, N. (2022). Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning. Plants, 11(20), 2668. doi: 10.3390/plants11202668
Fu, Y., Gou, L., & Hung, F. (2024). A lightweight CNN model for pepper leaf disease recognition in a human palm background. Journal of Plant Protection and Resources. https://www.sciencedirect.com/ science/ article/pii/ S2405844024094787
Janruang, N., and Unartngam, P. (2018). Morphological and Molecular Based Identification of Corn Downy Mildew Distributed in Thailand. International Journal of Agricultural Technology, 14(6), 845–860. https://www.thaiscience.info/Journals/Article/IJAT/10992417.pdf
Jung, M., Song, J.S., Shin, A.Y., Choi, B., Go, S., Kwon, S., Park J., & Kim,Y.M. (2023). Construction of deep learning-based disease detection model in multiple crops. Scientific Reports. 13(7331). https://www.nature. com/articles/s41598-023-34549-2
Parez, S., Dilshad, N., Alghamdi, N. S., Alanazi, T. M., and Lee, J. W. (2023). Visual intelligence in precision agriculture: exploring plant disease detection via efficient vision transformers. Sensors, 23(15), 6949. https://doi.org/10.3390/s23156949
Sun, X., Li, G., Qu, P., Xie, X., Pan, X., & Zang, W. (2022). Research on plant disease identification based on CNN. Cognitive Robotics, (2), 155-163. doi: 10.1016/j.cogr.2022.07.001
Thakur, P. S., Khanna, P., Sheorey, T., & Ojha, A. (2022). Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT. arXiv. https://arxiv.org/abs/2207.07919
Zhang, Y., et al. (2024). An ensemble of deep learning architectures for accurate plant leaf disease classification. Computers and Electronics in Agriculture. https://doi.org/10.37936/ecti-cit.2024181.254501
Zhou, Z., et al. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods. 17(22), https://doi.org/10.1186/s13007-021-00722-9
ศูนย์วิจัยข้าวโพดนครสวรรค์. (ม.ป.ป). (2567, 15 สิงหาคม). องค์ความรู้ข้าวโพดเลี้ยงสัตว์.https://www.doa.go.th /fc/nakhonsawan/?page_id=2321
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