A MULTI-CLASS DISEASE CLASSIFICATION MODEL FOR PLANT LEAVES BY A HYBRID-ENSEMBLE LEARNING TECHNIQUES
Main Article Content
Keywords
Abstract
Pathogens like bacteria and fungi could impact devastating consequences on agronomic productivity and even serve as catalysts for further disease outbreaks. Furthermore, the excessive utilization of fungicides and pesticides to manage this concern would both be costly and have a significant negative impact on the environment. Better crop sustainability could be achieved through the use of a technology that uses leaf sample images to identify various illnesses. The field of "Leaf Disease Detection (LDD)" has seen much research and development in classifying different types of leaf diseases. This research contributes to the development of an effective LDD system that is well suited to dealing with multi-class for diagnosing various leaf diseases. To classify several diseases in a plant leaf, this research introduces a novel "Ensemble Learning (EL)" methodology that combines the "ADA and GRADIENT Boosting (AGB)" techniques. Diseases that are induced by both fungi and bacteria could be more easily categorized with the help of this hybrid LDD model. For improving accuracy, the ADA architecture partitions the output's computed values before recombining them using the Gradient technique. With the use of "Particle Swarm Optimization (PSO)", the optimal feature would be selected and minimized to significantly improve classification accuracy. "Gradient Boosting" was primarily used to strengthen the weaker learning groups. Therefore, the most powerful AGB-trained model would categorize the leaf images under class labels such as "Alternaria Alternata", "Anthracnose", "Bacterial Blight", "Cercospora Leaf Spot", and "Healthy Leaves". Classifiers such as the "Bacterial Foraging Optimization based Radial Basis Function Neural Network (BRBFNN)", "Advanced K-Nearest Neighbor (AKNN)", and "Enhanced Support Vector Machine (ESVM)" all have been tested and compared to the proposed AGB classification model. Throughout the current research, we focus on four "Accuracy", "Recall (Sensitivity)", "Precision", and "F-measure" significant metrics for performance. The results demonstrate that the proposed AGB model for classification outperforms the current established approaches.
Downloads
References
[1]. Chiu, M. T., Xu, X., Wei, Y., Huang, Z., Schwing, A., Brunner, R., et al. (2020). Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (WA, USA: IEEE), 2825–2835. doi 10.1109/CVPR42600.2020.00290. [2]. Kurmi, Y., and Gangwar, S. (2022). A leaf image localization based algorithm for different crops disease classification. Inf. Process. Agric. 9 (3), 456–474. doi: 10.1016/j.inpa.2021.03.001. [3]. Fenu, G.; Malloci, F.M. Using Multi-Output Learning to Diagnose Plant Disease and Stress Severity. Complexity 2021, 2021, 18. [4]. Fenu, G.; Malloci, F.M. Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms. Big Data Cogn. Comput. 2021, 5, 2. [5]. Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2 (6), 1–20. doi: 10.1007/s42979-021-00815-1. [6]. Anjna, Sood, M., and Singh, P. K. (2020). Hybrid system for detection and classification of plant disease using qualitative texture features analysis. Proc. Comput. Sci. 167 (2019), 1056–1065. doi: 10.1016/j.procs.2020.03.404. [7]. Domingues, T., Brandão, T., and Ferreira, J. C. (2022). Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey. Agriculture 12 (9), 1350. doi: 10.3390/agriculture12091350. [8]. Liu, J., and Wang, X. (2021b). Plant diseases and pests detection based on deep learning: a review. Plant Methods 17 (1), 1–18. doi: 10.1186/s13007-021-00722-9. [9]. Peng, Y., and Wang, Y. (2022). Leaf disease image retrieval with object detection and deep metric learning. Front. Plant Sci. 13. doi: 10.3389/fpls.2022.963302. [10]. Wang, Y.; Wang, J.; Zhang, W.; Zhan, Y.; Guo, S.; Zheng, Q.; Wang, X. A survey on deploying mobile deep learning applications: A systemic and technical perspective. Digit. Commun. Netw. 2022, 8, 1–17. [11]. Wang, Z., and Zhang, S. (2018). Segmentation of corn leaf disease based on fully convolution neural network. Acad. J. Comput. Inf Sci. 1, 9–18. doi: 10.25236/AJCIS.010002. [12]. Wang, Q., Qi, F., Sun, M., Qu, J., and Xue, J. (2019). Identification of tomato disease types and detection of infected areas based on deep convolutional neural networks and object detection techniques. Comput. Intell. Neurosci. 2019, 9142753. doi: 10.1155/2019/9142753. [13]. Lin, K., Gong, L., Huang, Y., Liu, C., and Pan, J. (2019). Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front. Plant Sci. 10. doi: 10.3389/fpls.2019.00155. [14]. Kerkech, M., Hafiane, A., and Canals, R. (2020). Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Comput. Electron. Agric. 174, 105446. doi: 10.1016/j.compag.2020.105446. [15]. Shoaib, M., Hussain, T., Shah, B., Ullah, I., Shah, S. M., Ali, F., et al. (2022a). Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease. Front. Plant Sci. 13. doi: 10.3389/fpls.2022.1031748. [16]. Mrs. R. Dhivya, Dr. N. Shanmugapriya. (2021). A Comprehensive Review on the Identification and Classification of Leaf Diseases Model by their Various Stages. Design Engineering, Vol 2021: Issue 08, 10428-10444. Retrieved from https://www.thedesignengineering.com/index.php/DE/article/view/6102. [17]. R. Dhivya and N. Shanmugapriya, "An Analysis Study of Various Image Preprocessing Filtering Techniques based on PSNR for Leaf Images," 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), 2022, pp. 1-8, DOI: 10.1109/ICACTA54488.2022.9753444. [18]. S. S. Chouhan, A. Kaul, U. P. Singh, and S. Jain, "Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology," in IEEE Access, vol. 6, pp. 8852-8863, 2018, DOI: 10.1109/ACCESS.2018.2800685. [19]. R. Dhivya and N. Shanmugapriya. (2022). An Automated Plant Leaf Diseases Classification using AKMC and AKNN Machine Learning Techniques. JOURNAL OF ALGEBRAIC STATISTICS, 13(3), 3129–3142.
Article Sidebar
Article Details
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Transfer of Copyright and Permission to Reproduce Parts of Published Papers.
Authors retain the copyright for their published work. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.