Optimized Machine Learning System for Identifying Plant Diseases
Article Sidebar
Main Article Content
Plant diseases pose significant threats to agriculture, adversely affecting both crop yield and quality. This study offers a comprehensive overview of plant pathology, examining various types of diseases, their causative agents, and the intricate interactions between plants and pathogens. This study explored the integration of advanced deep learning and machine learning techniques. A dataset of plant leaf diseases, sourced from an online repository, was augmented with additional data featuring 11 West African plant species. The dataset underwent rigorous preprocessing to ensure compatibility with machine learning models. This study employed the ResNet50 Convolutional Neural Network (CNN) for feature extraction and XGBoost for classification, achieving a remarkable accuracy of 98.81% in differentiating between healthy and diseased plant leaves. The performance of the developed model was evaluated using key metrics, including accuracy, precision, recall, F1-score, confusion matrix, and ROC curve, and was found to outperform existing models in terms of accuracy. Furthermore, the model was successfully integrated into a mobile application, demonstrating efficient performance. This approach presents a scalable solution for precision agriculture, enhancing crop health management and boosting agricultural productivity.
Downloads
Downloads
References
Tai, A. P. K., Martin, M. V., & Heald, C. L. (2014). Threat to food security from climate change. Nature, 530(7591), 204–208.
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (2016). The assessment report on pollinators, pollination, and food production.
Strange, R. N., & Scott, P. R. (2005). Plant disease: A threat to global food security. Annual Review of Phytopathology, 43, 83–116. DOI: https://doi.org/10.1146/annurev.phyto.43.113004.133839
United Nations Environment Programme (UNEP). (2013). Smallholder agriculture in Africa.
Harvey, J., Duffy, B., & Ellis, R. (2014). Plant disease and smallholder farmers. Global Food Security, 3(3), 107–115.
Sanchez, P. A., & Swaminathan, M. S. (2005). Hunger in Africa: The link between agriculture and nutrition. Science, 307(5707), 1243–1245.
Food and Agriculture Organization (FAO). (2021). The state of food and agriculture.
International Telecommunication Union (ITU). (2015). Measuring the information society report.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. DOI: https://doi.org/10.3389/fpls.2016.01419
Anjna, M., Kumar, V., & Sharma, M. (2020). Image-based plant disease classification using deep learning methods: A review. Journal of Plant Pathology, 102(4), 945–962.
Genaev, A., Koryakov, I., & Melikhov, D. (2021). Automated plant disease detection using deep learning techniques. Computers and Electronics in Agriculture, 180, 105938.
Liu, X., Li, Y., & Zhang, Z. (2017). Plant disease detection with deep learning techniques. Journal of Computer Science and Technology, 32(1), 183–190.
Karthik, M., Karthikeyan, M., & Manoharan, P. (2020). Deep learning-based plant disease classification and identification. International Journal of Computer Applications, 175(18), 10–15.
Singh, A., & Misra, A. (2017). Detecting plant disease using convolutional neural networks. Journal of Computer Vision, 119(7), 655–663.
Khan, M. M., Ahmed, M. A., & Qureshi, H. (2021). A novel approach for plant disease detection using deep convolutional neural networks. Neural Computing and Applications, 33(14), 7887–7898.
Liu, H., & Wang, X. (2021b). Deep learning-based plant disease identification using convolutional neural networks. IEEE Access, 9, 14635–14645.
Ullah, M. F., Niazi, M. U., & Ullah, N. (2019). Challenges in deep learning for plant disease detection. Proceedings of the IEEE International Conference on Computer Vision, 2208–2216.
Ullah, N., Ali, M., & Khan, S. (2019). Hybrid CNN-RNN model for plant disease detection. Journal of Machine Learning Research, 20, 1-15.
Singh, A., & Misra, S. (2017). Deep belief network for plant disease detection. Computers and Electronics in Agriculture, 136, 140-150.
Karthik, R., Suresh, G., & Varma, R. (2020). SVM-based plant disease detection using texture and color features. Journal of Computer Science and Technology, 35(2), 237-247.
Genaev, M., Zhao, L., & Yang, H. (2021). Ensemble learning approach for plant disease classification. Artificial Intelligence Review, 54(3), 345-360.
Liu, J., & Wang, X. (2021). CNN with transfer learning for plant disease detection. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2282-2290. DOI: https://doi.org/10.1109/TNNLS.2020.3007412
Khan, A., Nawaz, M., & Ali, T. (2021). CNN with attention mechanisms for improved plant disease detection. International Journal of Computer Vision, 129(4), 1047-1061.
Anjna, N., Sharma, R., & Kumar, S. (2020). Hybrid CNN-decision tree model for plant disease detection. Machine Learning and Applications, 15(4), 225-239.
Liu, Y., Zhang, J., & Zhang, M. (2017). Deep learning-based plant disease detection using CNNs. Journal of Computational Biology, 24(9), 821-834.
Liu, T., Xu, H., & Zheng, Z. (2018). CNN with data augmentation for robust plant disease detection. Pattern Recognition Letters, 115, 83-90.
Kumar, A., Sharma, P., & Patel, V. (2019). Transfer learning with CNNs for plant disease detection. Journal of Agricultural Informatics, 10(2), 15-29.
Zhang, X., Yang, X., & Li, Z. (2020). Deep learning framework with feature fusion for plant disease detection. Neurocomputing, 399, 165-174. DOI: https://doi.org/10.1016/j.neucom.2020.07.039
Patel, S., Gupta, R., & Chauhan, N. (2022). Combining CNNs with LSTM networks for plant disease detection. Journal of Artificial Intelligence Research, 67, 287-303.
Wang, X., Sun, J., & Zheng, L. (2022). Comparative study of SVM and KNN for plant disease detection from images. Machine Learning and Data Mining, 18(3), 204-215.
Singh, V., Singh, A., & Gupta, A. (2023). Hybrid CNN-attention model for enhanced plant disease detection. Journal of Computer Vision and Image Analysis, 18(1), 74-89.
Sharma, M., Kumar, A., & Patel, R. (2023). Ensemble learning for plant disease detection: A comparative study. Artificial Intelligence in Agriculture, 28(4), 356-370.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.