Application of the Yolov8 Algorithm for Detecting Rice Plant Diseases with Web-Based Digital Images
DOI:
https://doi.org/10.30871/jaic.v10i2.12571Keywords:
YOLOv8, Rice Disease, Deep Learning, Image Classification, Environmental PollutionAbstract
The decline in environmental quality caused by industrial pollution and climate change has weakened the natural resistance of rice plants (Oryza sativa), increasing their susceptibility to various diseases. Conventional disease identification methods that rely on manual observation are often limited by subjectivity and human visual constraints. This study proposes a deep learning–based system for automatic rice leaf disease classification using the You Only Look Once version 8 (YOLOv8) architecture. The model was trained using a publicly available rice leaf image dataset consisting of 6,889 images categorized into eight classes: Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Sheath Blight, Narrow Brown Leaf Spot, Rice Hispa, and Healthy Rice Leaf. The research methodology includes image pre-processing, data augmentation, dataset splitting, and training using the YOLOv8n-cls model for 50 epochs. Experimental results demonstrate high classification performance with an accuracy of 99.5%, precision of 99%, recall of 98%, and an F1-score of 0.99. The trained model was then deployed into a web-based application that allows users to upload rice leaf images and obtain real-time disease classification results. The proposed system provides a practical tool to support early detection of rice plant diseases and assist farmers in improving crop management in modern agriculture.
Downloads
References
[1] S. Herliana, S. Ratnaningtyas, S. Nur Arifin, and N. Lawiyah, “Analysis of Indonesia’s Food Security Strategy: Rice Price Volatility,” Global Conference on Business and Social Sciences Proceeding, vol. 14, no. 2, pp. 1–1, Dec. 2022, doi: 10.35609/gcbssproceeding.2022.2(35).
[2] H. Agustina, B. Setiawan, M. Solahuddin, and Sugiyanta, “SRI (Rice Intensification System) water management of rice productivity,” IOP Conf. Ser. Earth Environ. Sci., vol. 542, no. 1, p. 012051, Jul. 2020, doi: 10.1088/1755-1315/542/1/012051.
[3] O. Aziz et al., “Irrigation methods affect water productivity, grain yield, and growth responses of rice at different levels of nitrogen,” J. Soil Water Conserv., vol. 73, no. 3, pp. 329–336, May 2018, doi: 10.2489/jswc.73.3.329.
[4] M. Barchia, H. Mareja, A. Susatya, and E. Putri, “Sustainability Of Rice Cultivation In Irrigated Paddy Fields In The Highlands Of Bengkulu, Indonesia,” in Proceedings of the 3rd Sriwijaya International Conference on Environmental Issues, SRICOENV 2022, October 5th, 2022, Palembang, South Sumatera, Indonesia, EAI, 2023. doi: 10.4108/eai.5-10-2022.2328294.
[5] R. Ardinata and W. M. Manurung, “Disaster from water pollution in Indonesia: Unsustainable human interaction with the environment and its social impacts,” ASEAN Natural Disaster Mitigation and Education Journal, vol. 2, no. 2, Jan. 2025, doi: 10.61511/andmej.v2i2.2025.1478.
[6] D. Desrizal, N. Carlo, and N. Syah, “The Impacts of PETI on the Batang Hari River to the Decline of Water Quality, Land Transfer Function, Socio-Cultural Life and the Community Economy,” Sumatra Journal of Disaster, Geography and Geography Education, vol. 3, no. 1, pp. 54–61, Jun. 2019, doi: 10.24036/sjdgge.v3i1.182.
[7] D. Kumar et al., “Impact of saline water irrigation on growth, yield and quality of rice (Oryza sativa L.) varieties,” International Journal of Research in Agronomy, vol. 7, no. 10, pp. 117–122, Oct. 2024, doi: 10.33545/2618060X.2024.v7.i10b.1709.
[8] A. Punia and S. K. Singh, “Contamination of water resources in the mining region,” in Contamination of Water, Elsevier, 2021, pp. 3–17. doi: 10.1016/B978-0-12-824058-8.00015-3.
[9] P. Prasher and M. Sharma, “An Introduction to Rice Diseases,” in Cereal Diseases: Nanobiotechnological Approaches for Diagnosis and Management, Singapore: Springer Nature Singapore, 2022, pp. 3–15. doi: 10.1007/978-981-19-3120-8_1.
[10] T. G. Devi, P. Neelamegam, and A. Srinivasan, “Rice plant disease, crop stages endemic and control measures - a survey,” International Journal of Advanced Intelligence Paradigms, vol. 23, no. 3/4, p. 399, 2022, doi: 10.1504/IJAIP.2022.126698.
[11] Hussain. A and B. Srikaanth. P, “Disease Classification and Detection Techniques in Rice Plant using Deep Learning,” in 2022 8th International Conference on Smart Structures and Systems (ICSSS), IEEE, Apr. 2022, pp. 1–7. doi: 10.1109/ICSSS54381.2022.9782162.
[12] P. Seelwal and A. Sharma, “Machine Vision Systems for Rice Diseases Detection: A Review,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, Apr. 2022, pp. 1686–1689. doi: 10.1109/ICACITE53722.2022.9823713.
[13] C. Gupta et al., “Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs),” BioData Min., vol. 18, no. 1, p. 91, Nov. 2025, doi: 10.1186/s13040-025-00497-y.
[14] K. Krisdianto, Elta Sonalitha, and Yandhika Surya Akbar Gumilang, “Deteksi penyakit padi menggunakan YOLO,” Uranus : Jurnal Ilmiah Teknik Elektro, Sains dan Informatika, vol. 2, no. 3, pp. 125–134, Jul. 2024, doi: 10.61132/uranus.v2i3.259.
[15] I. V. S. L. Haritha, M. Harshini, S. Patil, and J. Philip, “Real Time Object Detection using YOLO Algorithm,” in 2022 6th International Conference on Electronics, Communication and Aerospace Technology, IEEE, Dec. 2022, pp. 1465–1468. doi: 10.1109/ICECA55336.2022.10009184.
[16] Md. M. Hasan, “Rice Leaf Disease Dataset,” https://www.kaggle.com. [Online]. Available: https://www.kaggle.com/datasets/mdmehedihasan19/rice-leaf-disease-dataset/code/data
[17] S. Jatmika and D. E. Saputra, “Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method,” SinkrOn, vol. 7, no. 3, pp. 2008–2016, Aug. 2022, doi: 10.33395/sinkron.v7i3.11540.
[18] Ultralytics, “YOLOv8: State-of-the-Art YOLO Models,” https://docs.ultralytics.com. Accessed: Dec. 24, 2025. [Online]. Available: https://docs.ultralytics.com/models/yolov8/
[19] P. G. J. and N. K. V., “Google Colaboratory : Tool for Deep Learning and Machine Learning Applications,” Indian Journal of Computer Science, vol. 6, no. 3–4, p. 23, Aug. 2021, doi: 10.17010/ijcs/2021/v6/i3-4/165408.
[20] A. G. S. M. F. L. A. B. J. C. G. et al. Paszke, “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Advances in Neural Information Processing Systems (NeurIPS), Red Hook, NY, USA: Curran Associates, Inc., 2019, pp. 8024–8035. [Online]. Available: https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html
[21] X. Jia et al., “Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes,” Jul. 2018, [Online]. Available: http://arxiv.org/abs/1807.11205
[22] M. Praveena, V. B. Dubisetty, K. V. Varaprasad, M. Rama, P. S. Vadana, and T. S. R. Sai, “An In-Depth Analysis of Deep Learning and Machine Learning Methods for Identifying Rice Leaf Diseases,” in 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), IEEE, Sep. 2023, pp. 951–955. doi: 10.1109/ICOSEC58147.2023.10276335.
[23] M. Okty Dea Pratama and S. Suwarni, “Pengembangan Prototipe Desain User Interface & User Experience (UI/UX) Pada Aplikasi OSS URINDO Menggunakan FIGMA,” Jurnal Teknologi Informasi, vol. 8, no. 2, pp. 155–166, Dec. 2022, doi: 10.52643/jti.v8i2.2772.
[24] M. Simon, “Manipulating HTML Elements,” in JavaScript for Web Developers, Berkeley, CA: Apress, 2023, pp. 93–120. doi: 10.1007/978-1-4842-9774-2_3.
[25] C. Patil, “Crop Analyzer Using Deep Learning and Yolo,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 12, no. 3, pp. 3416–3421, Mar. 2024, doi: 10.22214/ijraset.2024.59651.
[26] H. L. Walingkas and P. O. N. Saian, “Penerapan Framework Flask pada Pembangunan Sistem Informasi Pemasok Barang,” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 7, no. 2, pp. 227–234, Apr. 2023, doi: 10.35870/jtik.v7i2.729.
[27] H. Saputra, K. Muchtar, N. Chitraningrum, A. Andria, and A. Febriana, “Performance evaluation of hyper-parameter tuning automation in YOLOV8 and YOLO-NAS for corn leaf disease detection,” SINERGI, vol. 29, no. 1, p. 197, Jan. 2025, doi: 10.22441/sinergi.2025.1.018.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Rakhmat Hidayat, Noora Qotrun Nada, Agung Handayanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
