Abstract
One of the most harmful viruses is Tomato yellow leaf curl virus (TYLCV), which is widespread over the world with tomato yellow leaf curl disease (TYLCD). It causes some symptoms to tomato leaf such as upward curling and yellowing. This paper introduces an efficient approach to detect and identify infected tomato leaves with these two viruses. The proposed approach consists of four main phases, namely pre-processing, image segmentation, feature extraction, and classification phases. Each input image is segmented and descriptor created for each segment. Some geometric measurements are employed to identify an optimal feature subset. Support vector machine (SVM) algorithm with different kernel functions is used for classification. The datasets of a total of 200 infected tomato leaf images with TSWV and TYLCV were used for both training and testing phase. N-fold cross-validation technique is used to evaluate the performance of the presented approach. Experimental results showed that the proposed classification approach obtained accuracy of 90 % in average and 92 % based on the quadratic kernel function.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peralta, I.E., Spooner, M.D., Razdan, M.K. Mattoo, A.K.: History, origin and early cultivation of tomato (Solanaceae). Genet Improv Solanaceous Crops Tomato 2 (2007)
Agrios, G.N.: Plant Pathology, 4th edn. Academic Press (1997)
Sikora, E.J.: Virus diseases of tomato. ANR-0836 (2011)
Rumpf T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W., Plümer L.: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 47(1), 91–99 (2010)
Hillnhuetter, C., Mahlein, A.K.: Early detection and localisation of sugar beet diseases: new approaches. Gesunde Pflanzen 60(4), 143–149 (2008)
Lin, W.T., Lin, C.H., Wu, T.H., Chan, Y.K.: Image segmentation using the k-means algorithm for texture features. World Acad. Sci. Eng. Technol. 65 (2010)
Goclawski, J., Sekulska-Nalewajko, J., Gajewska, E., Wielanek, M.: An automatic segmentation method for scanned images of wheat root systems with dark discolourations. Int. J. Appl. Math. Comput. Sci. 19(4), 679–689 (2009)
ISA, N.A.M.: Automated edge detection technique for Pap smear images using moving K-means clustering and modified seed based region growing algorithm. Int. J. Comput. Internet Manage. 13(3), 45–59 (2005)
Gonzales, R.C., Richard, E.W.: Digital image processing, 2nd edn. (2002)
Valliammal, N., Geethalakshmi, S.N.: Plant leaf segmentation using non linear K means clustering. Int. J. Comput. Sci. Issues (IJCSI) 9(3), 212–217 (2012)
Weizheng, S., Yachun, W., Zhanliang, C., Hongda, W.: Grading method of leaf spot disease based on image processing. In: Proceedings of IEEE International Conference on Computer Science and Software Engineering, vol. 6, pp. 491–494 (2008)
Sezgin, M.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)
Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern 9(1), 62–66 (1979)
Camargo, A., Smith, J.S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 102(1), 9–21 (2009)
Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of un-healthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR J. 15(1), 211–217 (2013)
Tian, J., Hu, Q., Ma, X.X., and Han, M.: An improved kpca/ga-svm classification model for plant leaf disease recognition. J. Comput. Inf. Syst. 8(18), 7737–7745 (2012)
Asraf, H.M., Nooritawati, M.T., Rizam, M.S.B.: A comparative study in kernel-based support vector machine of oil palm leaves nutrient disease. Procedia Eng. 41, 1353–1359 (2012)
Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recogn. 13(1), 3–16 (1981)
Vapnik, V.: The nature of statistical learning theory. Springer (2000)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)
Zhang, W., Jin, X.: Image recognition of wheat disease based on RBF support vector machine. In: Proceedings of the International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013). Atlantis Press (2013)
Subbaiah, V., Aparna, G.S., Gopal, D.V.R.S.: Computer aided molecular modeling approach of H & E (Haemotoxylin & Eosin) images of colon cancer. Int. J. Comput. Appl. 44(9), 5–8 (2012)
Legland, D., Kiêu, K., Devaux, M.F.: Computation of minkowski measures on 2d and 3d binary images. Image Anal. Stereology 26(2), 83–92 (2011)
Vanschoenwinkel, B., Manderick, B.: Appropriate kernel functions for support vector machine learning with sequences of symbolic data. Deterministic Stat. Methods Machine Learn. 256–280 (2005)
Boolchandani, D., Sahula, V.: Exploring efficient kernel functions for support vector machine based feasibility models for analog circuits. Int. J. Des. Anal. Tools Circuits Syst. 1(1) (2011)
Prekopcsák, Z., Henk, T, Gáspár-Papanek, C.: Cross-validation: the illusion of reliable performance estimation. In: RCOMM RapidMiner Community Meeting and Converence (2010)
Sikora, E.J., Gazaway, W.S.: Wilt Diseases of Tomatoes. Published by the Alabama Cooperative extension system. Reviewed for web June 2009, Anr-0797
Rojas, M.R., Kon, T.: First report of tomato yellow leaf curl virus associated with tomato yellow leaf curl disease in California. Am. Phytopathol. Soc. 91(8), 1056 (2007)
Al Bashish, D., Braik, M., Sulieman B.A.: Detection and classification of leaf diseases using K-means-based segmentation and neural-networks-based classification. Inf. Technol. J. 10(2), 267–275 (2011)
Phadikar, S., Sil, J., Das, A.K.: Classification of rice leaf diseases based on morphological changes. Int. J. Inf. Electron. Eng. 2, 460–463 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Mokhtar, U., Ali, M.A.S., Hassanien, A.E., Hefny, H. (2015). Identifying Two of Tomatoes Leaf Viruses Using Support Vector Machine. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 339. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2250-7_77
Download citation
DOI: https://doi.org/10.1007/978-81-322-2250-7_77
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2249-1
Online ISBN: 978-81-322-2250-7
eBook Packages: EngineeringEngineering (R0)