Abstract
This article introduces an efficient approach to detect and identify unhealthy tomato leaves using image processing technique. The proposed approach consists of three main phases; namely pre-processing, feature extraction, and classification phases. Since the texture characteristic is one of the most important features that describe tomato leaf, the proposed system system uses Gray-Level Co-occurrence Matrix (GLCM) for detecting and identifying tomato leaf state, is it healthy or infected. Support Vector Machine (SVM) algorithm with different kernel functions is used for classification phase. Datasets of total 800 healthy and infected tomato leaves images were used for both training and testing stages. N-fold cross-validation technique is used to evaluate the performance of the presented approach. Experimental results showed that the proposed classification approach has obtained classification accuracy of 99.83%, using linear kernel function.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrios, G.N.: Plant Pathology, 4th edn. Academic Press (1997)
Ying, Z., An, M., Zhang, X.: A foreign facility status and trends of development to fagriculture. J. Agriculture and Technology (2008)
Marathe, H.D., Kothe, P.N.: Leaf Disease Detection Using Image Processing Techniques. International Journal of Engineering Research & Technology (IJERT) 2(3) (March 2013)
Fathy, M.E., Hussein, A.S., Tolba, M.F.: Fundamental matrix estimation: a study of error criteria. Pattern Recognition Letters 32(2), 383–391 (2011)
Al-Bashish, D., Braik, M., Bani-Ahmad, S.: Detection and classification of leaf diseases using Kmeans-based segmentation and neural-networks-based classification. Inform. Technol. J. 10, 267–275 (2011)
Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W., Plumer, L.: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74(1), 91–99 (2010)
Hillnhuetter, C., Mahlein, A.K.: Early detection and localisation of sugar beet diseases: new approaches. Gesunde Pfianzen 60(4), 143–149 (2008)
Bauer, S.D., Korc, F., Frstner, W.: Investigation into the classification of diseases of sugar beet leaves using multispectral images. In: Henten, E.J.V., Goense, D., Lokhorst, C. (eds.) Precision Agriculture, pp. 229–238. Academic Publishers, Wageningen (2009)
Weizheng, S., Yachun, W., Zhanliang, C., Hongda, W.: Grading Method of Leaf Spot Disease Based on Image Processing. In: Proceedings of the 2008 International Conference on Computer Science and Software Engineering, CSSE, December 12-14, vol. 06, pp. 491–494. IEEE Computer Society, Washington, DC (2008)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–165 (2003)
Kumari, V.A., Chitra, R.: Classification Of Diabetes Disease Using Support Vector Machine. International Journal of Engineering Research and Applications (IJERA) 3(2), 1797–1801 (2013) ISSN: 2248-9622
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)
Camargo, A., Smith, J.S.: An image processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering 102(1), 9–21 (2009) ISSN 1537-5110
Liu, L., Zhang, W., Shu, S., Jin, X.: Image Recognition of Wheat Disease Based on RBF Support Vector Machine, In: Proceedings of International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013), Supported by the Key Technology Projects of Anhui Province, China (NO:1201a0301008) (2013)
Arivazhagan, S., Newlin Shebiah, R., Ananthi, S., Vishnu Varthini, S.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int.: CIGR Journal 15(1) (March 2013)
Phadikar, S., Sil, J., Das, A.K.: Classification of Rice Leaf Diseases Based on Morphological Changes. International Journal of Information and Electronics Engineering 2(3) (May 2012)
Tian, J., Hu, Q., Ma, X., Han, M.: An Improved KPCA/GA-SVM Classification Model for Plant Leaf Disease Recognition. Journal of Computational Information Systems, 7737–7745 (2012)
Asraf, H.M., Nooritawati, M.T., Shah Rizam, M.S.B.: A Comparative Study in Kernel-Based Support Vector Machine of Oil Palm Leaves Nutrient Disease. In: Proceedings of the International Symposium on Robotics and Intelligent Sensors 2012, International Symposium on Robotics and Intelligent Sensors, Procedia Engineering, vol. 41, pp. 1353–1359. Elsevier (2012)
Lu, H., Jiang, W., Ghiassi, M., Lee, S., Nitin, M.: Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques. PloS 7, e29704 (2012)
Albregtsen, F.: Statistical Texture Measures Computed from Gray Level Coocurrence Matrices (November 5, 2008)
V.N.: VAPNIK, The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)
Burges, C.J.C.: A tutorial on support vector machine for pattern recognition. Data Min. Knowl. Disc. 2(121) (1998)
Matheron, G.: Random Sets and Integral Geometry. Wiley, New York (1975)
Donohue, K.D., Huang, L., Burks, T.F., Forsberg, F., Piccoli, C.W.: Tissue classification with generalized spectrum parameters. Ultrasound Med. Biol. 27(11), 1505–1514 (2001)
Vanschoenwinkel, B., Manderick, B.: Appropriate kernel functions for support vector machine learning with sequences of symbolic data. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Machine Learning Workshop. LNCS (LNAI), vol. 3635, pp. 256–280. Springer, Heidelberg (2005)
Boolchandani, D., Sahula, V.: Exploring Efficient Kernel Functions for Support Vector Machine Based Feasibility Models for Analog Circuits. Int. Journal of Design, Analysis, and Tools for Circuits and Systems 1(1), 1–8 (2011)
Prekopcsk, Z., Henk, T., Gspr-Papanek, C.: Cross-validation: the illusion of reliable performance estimation. In: Proceedings of RCOMM 2010: RapidMiner Community Meeting and Conference, Dortmund, Nmetorszg, pp. 1–5 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Mokhtar, U. et al. (2015). SVM-Based Detection of Tomato Leaves Diseases. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-11310-4_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
eBook Packages: EngineeringEngineering (R0)