Abstract
Plants are everywhere in our lives, we can classify them by observing their features. But for ordinary people, the species we don’t know are much more than we know. So, for amateurs who are interested in botany, a system which can classify different species of leaves must be very useful, a system like that will also help students recognize the leaves they don’t know. This paper describes a system for leaf classification, which is developed with convolutional neural network technique. Previous researches in leaf identification usually use grayscale images. The main reason is that these samples mostly are green leaves. This system is trained by 1500 leaves to classify 50 kinds of plants. Compared to other research, our net use RGB images for input. And in convolutional neural network, we use PReLU instead of traditional ReLU. The experimental result shows that our method for classification gives accuracy of 94.8 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hinton, Geoffrey E., Salakhutdinov, Ruslan R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Ghodrati, A., Diba, A., Pedersoli, M., Tuytelaars, T., Van Gool, L.: DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers. arXiv:1510.04445
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS 2012) (2012)
Rashad, M.Z., El-Desouky, B.S., Khawasik, M.S.: Plants images classification based on textural features using combined classifier. Int. J. Comput. Sci. Inf. Technol. 3(4), 93–100 (2011)
Sumathi, C.S., Kumar, A.V.S.: Edge and texture fusion for plant leaf classification. Int. J. Comput. Sci. Telecommun. 3(6), 6–9 (2012)
He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance on imagenetclassification. arXiv preprint arXiv:1502.01852 (2015)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks, pp. 315–323 (2011)
Bengio, Y.: A connectionist approach to speech recognition. Int. J. Pattern Recogn. Artif Intell. 7(04), 647–667 (1993)
Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 61520106006, 61532008, 31571364, 61303111, 61411140249, 61402334, 61472280, 61472173, 61572447, 61373098, and 61572364, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wu, YH., Shang, L., Huang, ZK., Wang, G., Zhang, XP. (2016). Convolutional Neural Network Application on Leaf Classification. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-42291-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42290-9
Online ISBN: 978-3-319-42291-6
eBook Packages: Computer ScienceComputer Science (R0)