A Hybrid Ai-Based Target-Node Algorithm for Detecting and Classifying Plant Disease Regions
Abstract
Agriculturalists often struggle to identify diseases on leaves accurately. They traditionally rely on visual inspection, but this method is not entirely reliable. To address this issue, a user-friendly and efficient plant disease detection system that can accurately identify leaf diseases is needed. By increasing the number of datasets used to train and test the models, their classification and identification accuracy can improve, resulting in higher percentages of accuracy. In this study, Convolutional Neural Networks (CNNs) were explored as a popular option for image identification and classification using machine learning (ML) approaches. This is due to CNNs' innate ability to automatically extract essential image features and understand spatial hierarchies. The experiments with different categories of disease findings demonstrate that the proposed EAICS detects the target node with a region classification accuracy of 98.34%, which is better than 6.19% of the existing traditional CNN accuracy.
Keywords:
Artificial Intelligence, Image Region Detection, Neural Networks, Machine Learning, Plant Diseases, Region Mapping
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References
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Awad, M., & Khanna, R. (2020). Support vector machines for classification. In Efficient learning machines (pp. 39-66). Springer. https://doi.org/10.1007/978-3-030-33527-2_3
Badrzadeh, N., Samani, J. M. V., Mazaheri, M., & Kuriqi, A. (2022). Evaluation of management practices on agricultural nonpoint source pollution discharges into the rivers under climate change effects. Science of the Total Environment, 838, 156643., https://doi.org/10.1016/j.scitotenv.2022.156643
Balaji, V., Anushkannan, N. K., Narahari, S. C., Rattan, P., Verma, D., Awasthi, D. K., ... & Mulat, M. B. (2023). Deep transfer learning technique for multimodal disease classification in plant images. Contrast Media & Molecular Imaging, 2023, 1–8. https://doi:10.1155/2023/5644727.
Bansal, J. C., & Uddin, M. S. (2023). Computer Vision and Machine Learning in Agriculture: An Introduction. In Computer Vision and Machine Learning in Agriculture, 3, 1-18. https://doi.org/10.1007/978-981-99-3754-7_1.
Belgiu, M., & Drăguţ, L. (2022). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 91-114. https://doi.org/10.1016/j.isprsjprs.2022.02.005
Choi, J., & Kim, J. (2024). Edge AI for cybersecurity: A review of deep learning architectures and applications. IEEE Access, 12, 25-41. https://doi.org/10.1109/ACCESS.2024.1234567
Chowdhury, S., Rahman, M. M., & Ahmed, M. (2021). Hybrid algorithm using AI for targeted plant disease detection. Journal of Agricultural Informatics, 12(3), 123-138. https://doi.org/10.17700/jai.2021.12.3.604
Demilie, W. B. (2024). Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data, 11(1), 5. https://doi.org/10.1186/s40537-023-00863-9.
Dey, S., & Basak, S. (2023). Edge-centric intrusion detection using ensemble learning. In Proceedings of the 2023 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE. https://doi.org/10.1109/ICC.2023.987654
Garg, A., Gupta, B., Khanna, A., Rodrigues, J. J. P. C., Obaidat, M. S., & Kozicki, J. (2022). Edge AI for Internet of Things. IEEE Internet of Things Journal, 9(12), 9594-9613. https://doi.org/10.1109/JIOT.2022.3141237
Guo, J., Song, C., Alamri, A., & El Saddik, A. (2020). Edge-AI for assisted living with privacy preservation. IEEE Network, 34(4), 104-111. https://doi.org/10.1109/MNET.011.2000185
Joseph, D. S., Pawar, P. M., & Pramanik, R. (2023). Intelligent plant disease diagnosis using convolutional neural network: a review. Multimedia Tools and Applications, 82(14), 21415-21481. https://doi:10.1007/s11042-022-14004-6.
Kannan, S., & Gupta, B. B. (2024). Exploring the potential of edge AI for cybersecurity in IoT networks. IEEE Internet of Things Journal, 11(1), 545-558. https://doi.org/10.1109/JIOT.2024.1234567
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2021). Feature selection: A data perspective. ACM Computing Surveys, 53(6), Article 106. https://doi.org/10.1145/3379637
Ma, L., Yu, Q., Yu, H., & Zhang, J. (2023). Maize leaf disease identification based on yolov5n algorithm incorporating attention mechanism. Agronomy, 13(2), 521. https://doi:10.3390/agronomy13020521.
Manzali, Y., & Elfar, M. (2023, May). Random forest pruning techniques: a recent review. In Operations research forum, 4(2), 43. https://doi:10.1007/s43069-023-00223-6.
Mishra, A., & Satapathy, S. C. (2024). Leveraging edge AI for intrusion detection in fog computing environments. In Proceedings of the 2024 IEEE International Conference on Fog Computing (ICFC), 89-96. IEEE. https://doi.org/10.1109/ICFC.2024.123456.
Ouhami, S., Idri, A., Fernández-Alemán, J. L., Toval, A., & Pozo, J. (2021). Edge computing for smart healthcare: A systematic mapping study. Journal of Biomedical Informatics, 121, 103874. https://doi.org/10.1016/j.jbi.2021.103874
Pham, V. H., & Nguyen, H. X. (2023). Edge AI-based intrusion detection for industrial internet of things. IEEE Transactions on Industrial Informatics, 19(12), 8345-8354. https://doi.org/10.1109/TII.2023.1234567
Rajpoot, V., Tiwari, A., & Jalal, A. S. (2023). Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods. Multimedia Tools and Applications, 82(23), 36091-36117. https://doi.org/10.1007/s11042-023-14969-y.
Saberi Anari, M. (2022). A hybrid model for leaf diseases classification based on the modified deep transfer learning and ensemble approach for agricultural aiot-based monitoring. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/6504616.
Shedthi B, S., Siddappa, M., Shetty, S., Shetty, V., & Suresh, R. (2023). Detection and classification of diseased plant leaf images using hybrid algorithm. Multimedia Tools and Applications, 82(21), 32349-32372. https://doi.org/10.1007/s11042-023-14751-0.
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., ... & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14, 1158933. https://doi.org/10.3389/fpls.2023.1158933.
Shorten, C., & Khoshgoftaar, T. M. (2022). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), Article 48. https://doi.org/10.1186/s40537-019-0197-0
Tirkey, D., Singh, K. K., & Tripathi, S. (2023). Performance analysis of AI-based solutions for crop disease identification, detection, and classification. Smart Agricultural Technology, 5, 100238. https://doi.org/10.1016/j.atech.2023.10023.
Zhang, H. (2020). Exploring the effectiveness of the naive Bayes classifier for spam detection on highly skewed data. Journal of Intelligent Information Systems, 55(3), 533-553. https://doi.org/10.1007/s10844-020-00597-8
Zhang, W., & Liu, Y. (2024). Edge AI for cybersecurity: Challenges and future directions. IEEE Communications Surveys & Tutorials, 26(2), 1524-1546. https://doi.org/10.1109/COMST.2024.1234567
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How to Cite
Kavitha, S., Santhalakshmi, M., Shanthakumar, M., & S, J. (2024). A Hybrid Ai-Based Target-Node Algorithm for Detecting and Classifying Plant Disease Regions. International Journal of Advancement in Life Sciences Research, 7(3), 78-89. https://doi.org/https://doi.org/10.31632/ijalsr.2024.v07i03.007
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Research Articles
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