Abstract
Recently, deep learning is very popular, it has been applied into many applications, In this paper, a new neural network, hybrid deep learning is introduced, which included AutoEncoder(AE) and convolutional neural network (CNN). This neural network is applied for extracting the features of the plant leaves. In this paper, we proved that hybrid deep learning can extract better features for classification task. We apply the hybrid deep learning to extract features of leaf pictures, and then we classify leaves using those features with SVM, the result suggests that this method is not only better than pure SVM, but also better than pure AE and pure CNN.
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Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61373098 and 61272333, China Postdoctoral Science Foundation Grant, Nos. 2014M561513, and partly supported by the National High-Tech R&D Program (863) (2014AA021502 & 2015AA020101), and the grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120072110040), and the grant from the Outstanding Innovative Talent Program Foundation of Henan Province, No. 134200510025.
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Liu, Z. et al. (2015). Hybrid Deep Learning for Plant Leaves Classification. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_11
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DOI: https://doi.org/10.1007/978-3-319-22186-1_11
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