Performance analysis of support vector machine for early identification of citrus diseases
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Abstract
Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel.
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Citrus, Kernal, Machine learning, Plant disease, Support vector machine
Arivazhagan, S., Shebiah, R. N., Ananthi, S. & Varthini, S. V. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211–217.
Asraf, H. M., Nooritawati, M. T. & Rizam, M. S. (2012). A comparative study in kernel-based support vector machine of oil palm leaves nutrient disease. Procedia Engineering, 41, 1353–1359.
Barman, U. & Choudhury, R. D. (2021). Smartphone assist deep neural network to detect the citrus diseases in agri-informatics. Global Transitions Proceedings.
Barman, U., Choudhury, R. D., Sahu, D. & Barman, G. G. (2020). Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Computers and Electronics in Agriculture, 177, 105661.
Barman, U., Choudhury, R. D., Saud, A., Dey, S., Pratim, M. B. & Gunjan, B. G. (2018). Estimation of chlorophyll using image processing. Int J Recent Sci Res, 9(3), 24850–24853.
Barman, U., Pathak, C. & Mazumder, N. K. (2023). Comparative assessment of Pest damage identification of coconut plant using damage texture and color analysis. Multimedia Tools and Applications, 1–23.
Dananjayan, S., Tang, Y., Zhuang, J., Hou, C. & Luo, S. (2022). Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images. Computers and Electronics in Agriculture, 193, 106658.
Deng, X., Lan, Y., Hong, T. & Chen, J. (2016). Citrus greening detection using visible spectrum imaging and C-SVC. Computers and Electronics in Agriculture, 130, 177–183.
Devi, T. G., Neelamegam, P. & Srinivasan, A. (2018). Plant leaf disease detection using K means segmentation. Int. J. Pure Appl. Math, 119(15), 3477–3483.
Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M. M., Dhaou, I. B., & Iwendi, C. (2022). A novel deep learning model for detection of severity level of the disease in citrus fruits. Electronics, 11(3), 495.
Doh, B., Zhang, D., Shen, Y., Hussain, F. Doh, R. F., & Ayepah, K. (2019). Automatic citrus fruit disease detection by phenotyping using machine learning. 2019 25th International Conference on Automation and Computing (ICAC), 1–5.
Gavhale, K. R., Gawande, U. & Hajari, K. O. (2014). Unhealthy region of citrus leaf detection using image processing techniques. International Conference for Convergence for Technology-2014, 1–6.
Iqbal, Z., Khan, M. A., Sharif, M., Shah, J. H. ur Rehman, M. H., & Javed, K. (2018). An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and Electronics in Agriculture, 153, 12–32.
Khirade, S. D. & Patil, A. B. (2015). Plant disease detection using image processing. 2015 International Conference on Computing Communication Control and Automation, 768–771.
Patil, J. K. & Kumar, R. (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food, 10(2), 69–78.
Poojary, H. & Shabari, S. B. (2018). A survey on plant disease detection using support vector machine. 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), 292–295.
Pujari, D., Yakkundimath, R. & Byadgi, A. S. (2016). SVM and ANN based classification of plant diseases using feature reduction technique. IJIMAI, 3(7), 6–14.
Sharif, M., Khan, M. A., Iqbal, Z., Azam, M. F., Lali, M. I. U. & Javed, M. Y. (2018). Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Computers and Electronics in Agriculture, 150, 220–234.
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D. & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016.
Sunny, S. & Gandhi, M. I. (2018). An efficient citrus canker detection method based on contrast limited adaptive histogram equalization enhancement. Int. J. Appl. Eng. Res, 13(1), 809–815.
Zhang, M. & Meng, Q. (2011). Automatic citrus canker detection from leaf images captured in field. Pattern Recognition Letters, 32(15), 2036–2046.
Zhang, S., You, Z. & Wu, X. (2019). Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Computing and Applications, 31, 1225–1232.
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