Abstract
In this paper, a plant identification approach using 2D digital images of leaves is proposed. This approach will be used to develop an expert system to identify plant species by processing colored images of its leaf. The approach made use of feature fusion technique and the Bagging classifier. Feature fusion technique is used to combine color, shape, and texture features. Color moments, invariant moments, and Scale Invariant Feature Transform (SIFT) are used to extract the color, shape, and texture features, respectively. Linear Discriminant Analysis (LDA) is used to reduce the number of features and Bagging ensemble is used to match the unknown image and the training or labeled images. The proposed approach was tested using Flavia dataset which consists of 1907 colored images of leaves. The experimental results showed that the accuracy of feature fusion approach was much better than all other single features. Moreover, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.
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References
Gaber, T., Tharwat, A., Snasel, V., Hassanien, A.E.: Plant identification: two dimensional-based versus one dimensional-based feature extraction methods. In: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, 375–385. Springer (2015)
Liu, J., Zhang, S., Deng, S.: A method of plant classification based on wavelet transforms and support vector machines. In: Proceedings of the 5th international conference on Emerging intelligent computing technology and applications (pp. 253–260). Springer-Verlag (2009)
Satti, V., Satya, A., Sharma, S.: An automatic leaf recognition system for plant identification using machine vision technology. Int. J. Eng. Sci. Technol. 5(4), 874–879 (2013)
Caglayan, A., Guclu, O., Can, A.B.: A plant recognition approach using shape and color features in leaf images. In: International Conference on Image Analysis and Processing (ICIAP), pp. 161–170. Springer (2013)
Arun Priya, C., Balasaravanan, T., Thanamani, A.S.: An efficient leaf recognition algorithm for plant classification using support vector machine. In: International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), pp. 328–432. IEEE (2012)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Tharwat, A., Gaber, T., Hassanien, A.E., Shahin, M., Refaat, B.: Sift-based arabicsign language recognition system. In: Afro-European Conference for Industrial Advancement, pp. 359–370. Springer (2015)
Tharwat, A., Gaber, T., Hassanien, A.E., Hassanien, H.A., Tolba, M.F.: Cattle identification using muzzle print images based on texture features approach. In: Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, pp. 217–227. Springer (2014)
Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: Wld: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)
Cheung, W., Hamarneh, G.: n-SIFT: n-dimensional scale invariant feature transform. IEEE Trans. Image Process. 18(9), 2012–2021 (2009)
Tharwat, A., Gaber, T., Hassanien, A. E.: Cattle identification based on muzzle images using gabor features and SVM classifier. In: Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, November 28–30, 2014. Proceedings (Vol. 488, p. 236). Springer (2014)
Ibrahim, A., Tharwat, A.: Biometric authenticationmethods based on ear and finger knuckle images. Int. J. Comput. Sci. Issues (IJCSI) 11(3), 134–138 (2014)
Semary, N.A., Tharwat, A., Elhariri, E., Hassanien, A.E.: Fruit-based tomato grading system using features fusion and support vector machine. In: Intelligent Systems’ 2014, pp. 401–410. Springer (2015)
Tharwat, A., Ibrahim, A.F., Ali, H., et al.: Multimodal biometric authentication algorithm using ear and finger knuckle images. In: Seventh International Conference on Computer Engineering and Systems (ICCES), pp. 176–179. IEEE (2012)
Tharwat, A., Ibrahim, A., Hassanien, A. E., Schaefer, G.: Ear recognition using block-based principal component analysis and decision fusion. In: Proceedings of the 6th international conference, PReMI 2015, Warsaw, Poland. Lecture Notes in Computer Science, Vol. 9124, pp. 246–254 (2015)
Tharwat, A., Ibrahim, A., Ali, H.A.: Personal identification using ear images based on fast and accurate principal component analysis. In: 8th International Conference on Informatics and Systems (INFOS), pp. 56–59 (2012)
Nath, S.S., Mishra, G., Kar, J., Chakraborty, S., Dey, N.: A survey of image classification methods and techniques. In: International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 554–557. IEEE (2014)
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Tharwat, A., Gaber, T., Awad, Y.M., Dey, N., Hassanien, A.E. (2016). Plants Identification Using Feature Fusion Technique and Bagging Classifier. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_41
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DOI: https://doi.org/10.1007/978-3-319-26690-9_41
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