Abstract
Our society is becoming increasingly reliant on technology every day. Agriculture, on the other hand, is critical to human survival. Rice is one of the most important grains of food. It feeds nearly half of the world’s population and supports a large number of jobs. Crop diseases cause significant yield losses around the world, particularly in Sub Saharan Africa. To implement effective disease management measures, pathogen detection and understanding spatiotemporal dynamics are critical, and this necessitates the use of molecular detection methods, particularly to discriminate between infections that cause similar symptoms. As a result, adequate disease mitigation for rice plants is critical. We used to our proposes a model for detecting three rice leaf diseases: brown spot, leaf smut, and bacterial leaf blight. This paper presents a unique model for the classification of rice leaf diseases by multi-class SVM, K—means clustering, and PSO. The disease was classified using an SVM classifier and the classifier accuracy is optimized using PSO. The exploratory results show that the proposed approach performed well in terms of disease detection accuracy, with a score of 98.89%.
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References
International Rice Research Institute, “Bacterial blight-IRRI Rice Knowledge Bank,” Rice Knowledge Bank. [Online]. Available: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/bacterial-blight. Accessed 28 Nov 2019
N. Nandhini, R. Bhavani, Feature extraction for diseased leaf image classification using machine learning. International Conference on Computer Communication and Informatics (ICCCI) (2020), pp. 11–4
A. Elaraby, W. Hamdy, M. Alruwaili, Optimization of deep learning model for plant disease detection using particle swarm optimizer. CMC-Comput. Mater. Continua 71(2), 4019–4031 (2022)
International Rice Research Institute, “Brown spot-IRRI Rice Knowledge Bank,” Rice Knowledge Bank. [Online]. Available: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/brown-spot. Accessed 28 Nov 2019
W. Hamdy, I. Elansary, A. Darwish, A.E. Hassanien, An optimized classification model for COVID-19 pandemic based on convolutional neural networks and particle swarm optimization algorithm. Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, (2021) pp. 43–61
W. Hamdy, I. Elansary, A. Darwish, A.E. Hassanien, Artificial intelligence strategy in the age of Covid-19: opportunities and challenges. Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches (2021), pp. 81–93
J.G.A. Barbedo, Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus 2(1), 1–12 (2013)
J.P. Shah, H.B. Prajapati, V.K. Dabhi, A survey on detection and classification of rice plant diseases. In 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC). IEEE (2016), pp. 1–8
S. Shimamura, K. Uehara, S. Koakutsu, Automatic identification of plant physiological disorders in plant factories with artificial light using convolutional neural networks. Int. J. New Comput. Architect. Appl. 9(1), 25–31 (2019)
M. Sugimoto, K. Takeuchi, T. Yamaji, H. Inoue, K. Hosotani, Y. Inoki, D. Yoshioka, M. Endo, M. Kato, T. Shirakawa, H. Fukita, A study for development of autonomous paddy-weeding robot system-an experimentation for autonomously straight-running based on compass-compensation. Int. J. New Comput. Architect. Appl. 8(4), 186–198 (2018)
S.D. Khirade, A.B. Patil, Plant disease detection using image processing. In 2015 International Conference on Computing Communication Control and Automation. (IEEE, 2015), pp. 768–771
H. Wang, G. Li, Z. Ma, X. Li, Image recognition of plant diseases based on backpropagation networks. In 2012 5th International Congress on Image and Signal Processing . (IEEE, 2012), pp. 894–900
J.O. William, J.D. Cruz, L. Agapito, J.S. Paul, I. Valenzuela, Identification of diseases in rice plant (Oryza sativa. L) using back propagation artificial neural network. In International Conference on HNICEM (2014), pp. 1–6
T. Suman, T. Dhruvakumar, Classification of paddy leaf diseases using shape and color features. IJEEE 7(01), 239–250 (2015)
A.K. Singh, A. Rubiya, B.S. Raja, Classification of rice disease using digital image processing and svm classifier. Int. J. Electric. Electron. Eng. 7(1), 294–299 (2015)
P.K. Sethy, B. Negi, N. Bhoi, Detection of healthy and defected diseased leaf of rice crop using K-means clustering technique. Int. J. Comput. Appl. 157(1), 24–27 (2017)
Q. Yao, Z. Guan, Y. Zhou, J. Tang, Y. Hu, B. Yang, Application of support vector machine for detecting rice diseases using shape and color texture features. In 2009 International Conference on Engineering Computation (IEEE, 2009), pp. 79–83
S. Phadikar, J. Sil, A.K. Das, Classification of rice leaf diseases based on morphological changes. Int. J. Inf. (2012)
S.S. Chawathe, Rice disease detection by image analysis. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (IEEE, 2020), pp. 0524–0530
A. Elaraby, W. Hamdy, H. Nisar, M.H. Alkinani, Segmentation of Activated Sludge Flocs in Microscopic Images for Monitoring Wastewater Treatment. Complexity (2022)
H.B. Prajapati, J.P. Shah, V.K. Dabhi, Detection and classification of rice plant diseases. Intell. Decision Technol. 11(3), 357–373 (2017)
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Hamdy, W., Ismail, A., Awad, W.A., Ibrahim, A.H., Hassanien, A.E. (2023). A Support Vector Machine Model for Rice (Oryza sativa L.) Leaf Diseases Based on Particle Swarm Optimization. In: Hassanien, A.E., Soliman, M. (eds) Artificial Intelligence: A Real Opportunity in the Food Industry. Studies in Computational Intelligence, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-031-13702-0_4
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