Abstract
In automatic plant classification, plant identification based on digital leaf images is a challenging task. This paper proposes a Moth-Flame Optimization (MFO)-based deep belief network (DBN) method for plant leaf recognition. Initially, a combination of texture and shape features is applied for extracting features from preprocessed image. Further, for leaf classification, the MFO optimizes the DBN parameters to minimize error and is used as classifier. The classifier has been applied to five different sets of mango leaf images and achieved an accuracy of 98.5%. The experimental result indicates that it is feasible to automatically classify plants by using multi-feature extraction of plant leaf images in combination with MFO-based DBN.
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References
Chaki, J., Parekh, R., Bhattacharya, S.: Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recogn. Lett. 58, 61–68 (2015)
Cope, J., Corney, D., Clark, J., Remagnino, P., Wilkin, P.: Plant species identification using digital morphometrics: a review. Expert Syst. Appl. 39(8), 7562–7573 (2012)
Liu, J., Zhang, S., Deng, S.: A method of plant classification based on wavelet transforms and support vector machines. Emerg. Intell. Comput. Technol. Appl. 253–260 (2009)
Tang, Z., Su, Y., Er, M., Qi, F., Zhang, L., Zhou, J.: A local binary pattern based texture descriptors for classification of tea leaves. Neurocomputing 168, 1011–1023 (2015)
Zhu, X., Zhu, M., Ren, H.: Method of plant leaf recognition based on improved deep convolutional neural network. Cogn. Syst. Res. 52, 223–233 (2018)
Hu, J., Chen, Z., Yang, M., Zhang, R., Cui, Y.: A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process. Lett. 25(6), 853–857 (2018)
Abdel-Zaher, A., Eldeib, A.: Breast cancer classification using deep belief networks. Expert Syst. Appl. 46, 139–144 (2016)
Kahou, S., Bouthillier, X., Lamblin, P., Gulcehre, C., Michalski, V., Konda, K., Jean, S., Froumenty, P., Dauphin, Y., Boulanger-Lewandowski, N., Chandias Ferrari, R., Mirza, M., Warde-Farley, D., Courville, A., Vincent, P., Memisevic, R., Pal, C., Bengio, Y.: EmoNets: multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 10(2), 99–111 (2015)
Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hinton, G.: A practical guide to training restricted boltzmann machines. Lecture Notes in Computer Science, pp. 599–619 (2012)
Lu, J., Wang, G., Moulin, P.: Localized multifeature metric learning for image-set-based face recognition. IEEE Trans. Circuits Syst. Video Technol. 26(3), 529–540 (2016)
Lu, J., Zhou, X., Tan, Y.-P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 331–345 (2014)
Tiwari, D., Tyagi, V.: Dynamic texture recognition based on completed volume local binary pattern. Multidimension. Syst. Signal Process. 27(2), 563–575 (2015)
Shen, L., Ji, Z.: Gabor wavelet selection and SVM classification for object recognition. Acta Automatica Sin. 35(4), 350–355 (2009)
Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015)
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Pankaja, K., Suma, V. (2020). Mango Leaves Recognition Using Deep Belief Network with Moth-Flame Optimization and Multi-feature Fusion. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 159. Springer, Singapore. https://doi.org/10.1007/978-981-13-9282-5_3
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DOI: https://doi.org/10.1007/978-981-13-9282-5_3
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