LUNTIAN: Optimizing Crop Health Utilizing YOLOv8 Object Detection Algorithm for Plant Disease Detection

This study focuses on developing a user-friendly and cost-effective diagnostic system designed to assist agricultural practitioners in monitoring plant health. The system integrates a machine learning model based on YOLOv8 (You Only Look Once version 8) for accurate plant disease classification using image data. Remote sensing techniques are employed to enable early disease detection, utilizing Raspberry Pi 5 equipped with soil moisture, humidity, and temperature sensors, AI Chatbot along with a webcam for image-based plant disease detection. Real-time data is transmitted to a web platform for visualization and analysis. The detection model employs a Convolutional Neural Network (CNN) and YOLOv8 for high-accuracy classification, evaluated using precision, recall, and mean average precision (mAP) to ensure robust performance across multiple plant disease categories. A web-based application was also developed to allow real-time health monitoring, data visualization, and storage of diagnostic results. Additionally, a database of disease symptoms and management practices was established to support informed decision-making and promote sustainable crop management. The YOLOv8 object detection algorithm effectively identified diseases like Mosaic Virus and Powdery Mildew, with improved precision, recall, and mAP scores. The web platform enhanced user engagement, offering real-time monitoring, data storage, and insights for informed decision-making.

AI Chatbot, Object Detection, Plant Disease Detection, Smart Farming

1. Trinh, D. C., Mac, A. T., Dang, K. G., Nguyen, H. T., Nguyen, H. T., & Bui, T. D. (2024). Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection. AgriEngineering, 6(1), 2024, pp. 302-317. https://doi.org/10.3390/agriengineering6010018. [Google Scholar] [Crossref]

2. Orchi, H., Sadik, M., Khaldoun, M., & Sabir, E. (2023, June). Real-time Detection of Crop Leaf Diseases Using Enhanced YOLOv8 Algorithm. International Wireless Communications and Mobile Computing (IWCMC), 2023 (pp. 1690-1696). IEEE. https://doi.org/10.1109/IWCMC58020.2023.10182573. [Google Scholar] [Crossref]

3. Qadri, S. A. A., Huang, N. F., Wani, T. M., & Bhat, S. A. Plant Disease Detection and Segmentation using End-to-End YOLOv8: A Comprehensive Approach. IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), 2023, pp. 155-160. IEEE. https://doi.org/10.1109/ICCSCE58721.2023.10237169. [Google Scholar] [Crossref]

4. Wang, X., Liu, J. Vegetable Disease Detection Using an Improved YOLOv8 Algorithm in the Greenhouse Plant Environment. Sci Rep 14, 2024, 4261. https://doi.org/10.1038/s41598-024-54540-9. [Google Scholar] [Crossref]

5. Abid, M. S. Z., Jahan, B., Al Mamun, A., Hossen, M. J., & Mazumder, S. H. Bangladeshi Crops Leaf Disease Detection Using YOLOv8. Heliyon, 10, 2024, (18), https://doi.org/10.1016/j.heliyon.2024.e36694. [Google Scholar] [Crossref]

6. Jackulin, C., & Murugavalli, S. A Comprehensive Review on Detection of Plant Disease using Machine Learning and Deep Learning Approaches. Measurement: Sensors, 24, 2022, 100441. https://doi.org/10.1016/j.measen.2022.100441. [Google Scholar] [Crossref]

7. Alatawi, A. A., Alomani, S. M., Alhawiti, N. I., & Ayaz, M. Plant disease detection using AI based VGG-16 model. International Journal of Advanced Computer Science and Applications, 2022, 13(4). https://doi.org/10.14569/IJACSA.2022.0130484. [Google Scholar] [Crossref]

8. Sujatha, R., Chatterjee, J. M., Jhanjhi, N. Z., & Brohi, S. N. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 2021, https://doi.org/10.1016/j.micpro.2020.103615. [Google Scholar] [Crossref]

9. Panchal, A. V., Patel, S. C., Bagyalakshmi, K., Kumar, P., Khan, I. R., & Soni, M.. Image-based Plant Diseases Detection Using Deep Learning. Materials Today: Proceedings, 80, 2021, pp. 3500-3506. https://doi.org/10.1016/j.matpr.2021.07.281. [Google Scholar] [Crossref]

10. Badgujar, C. M., Poulose, A., & Gan, H. Agricultural Object Detection with You Only Look Once (YOLO) Algorithm: A Bibliometric and Systematic LiterSature Review. Computers and Electronics in Agriculture, 223, 2024, 109090. https://doi.org/10.1016/j.compag.2024.109090. [Google Scholar] [Crossref]

11. Alif, M. A. R., & Hussain, M. YOLOv1 to YOLOv10: A Comprehensive Review of YOLO Variants and their Application in the Agricultural Domain. arXiv Preprint, 2024, arXiv:2406.10139. https://doi.org/10.48550/arXiv.2406.10139. [Google Scholar] [Crossref]

12. Sonawane, S., & Patil, N. N. Comparative performance analysis of YOLO object detection algorithms for weed detection in agriculture. Intelligent Decision Technologies, (Preprint), 2024, pp. 1-13. https://doi.org/10.3233/IDT-240978. [Google Scholar] [Crossref]

13. Cuong, N. H. H., Trinh, T. H., Meesad, P., & Nguyen, T. T.. Improved YOLO Object Detection Algorithm to Detect Ripe Pineapple Phase. Journal of Intelligent & Fuzzy Systems, 43(1), 2022, pp. 1365-1381. https://doi.org/10.3233/JIFS-213251. [Google Scholar] [Crossref]

14. Lippi, M., Bonucci, N., Carpio, R. F., Contarini, M., Speranza, S., & Gasparri, A.. A YOLO-based Pest Detection System for Precision Agriculture. 29th Mediterranean Conference on Control and Automation (MED) 2022, (pp. 342-347). https://doi.org/10.1109/MED51440.2021.9480344. [Google Scholar] [Crossref]

15. Ahmad, B., Noon, S. K., Ahmad, T., Mannan, A., Khan, N. I., Ismail, M., & Awan, T. Efficient Real-Time Detection of Plant Leaf Diseases Using YOLOv8 and Raspberry Pi. VFAST Transactions on Software Engineering, 12(2), 250-259. https://doi.org/10.21015/vtse.v12i2.1869. [Google Scholar] [Crossref]

16. Kavaliauskas, M., & Sledevič, T. Identification of Tomato Leaf Disease using YOLOv8 Detection Models on GPU and Raspberry Pi. IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream) 1501 MR, 2024. pp. (1-3). https://doi.org/10.1109/eStream61684.2024.10542533 [Google Scholar] [Crossref]

17. Dinesh, R., Mohan, H., Kumar, A. S., Mathai, A., & Deepak, S. Autonomous IoT-Integrated Tomato Plant Disease Detection: Harnessing YOLOv8 Algorithm and Micro-Navigation for Precision Agriculture. IEEE Recent Advances in Intelligent Computational Systems (RAICS) 2024, (pp. 1-6). https://doi.org/10.1109/RAICS61201.2024.10689940. [Google Scholar] [Crossref]

18. Aftab, S., Lal, C., Beejal, S. K., & Fatima, A. Raspberry Pi (Python AI) For Plant Disease Detection. Int. J. Curr. Res. Rev, 14, 2022, pp (36-42) http://dx.doi.org/10.31782/IJCRR.2022.14307. [Google Scholar] [Crossref]

19. Soetedjo, A., & Hendriarianti, E. Plant Leaf Detection and Counting in a Greenhouse During Day and Night Time using A Raspberry Pi NoIR Camera. Sensors, 21(19), 2021, 6659. https://doi.org/10.3390/s21196659 [Google Scholar] [Crossref]

20. Sankar, M., Mudgal, D. N., & Jalinder, M. M. Green Leaf Disease Detection Using Raspberry Pi. 1st International Conference on Innovations in Information and Communication Technology (ICIICT), 2019, (pp. 1-6). IEEE. https://doi.org/10.1109/ICIICT1.2019.8741508. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study