Abstract
Compared with traditional flower recognition methods, the existing flower recognition applications on the market use advanced deep learning technology to improve the accuracy of plant recognition and solve the problem of plant recognition. The article studied the five applications that users commonly use, comparing and analyzing their recognition accuracy, and finally putting forward the feasibility advice for further improvement of flower recognition applications. The method of sampling survey was adopted, this paper divides the garden flowers and wild flowers into different levels according to their common degrees. Each type of flower was shot from 5 different angles and scenes, and recognized by these five applications separately. The results showed that the rankings of the five applications evaluated were Hua Bangzhu, Hua Banlv, Xing Se, Microsoft’s Flower Recognition, and Find Flower Recognition. At pre-sent, it is necessary to continuously improve from the aspects of technology, products and plant libraries.
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
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. J. Adv. Neural Inf. Process. Syst. 25, 1106–1114 (2012)
Hinton, G.E., Osindero, S., The, Y.: A fast learning algorithm for deep belief nets. J. Neural Comput. 18, 1527–1554 (2006)
LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. J. Proc. IEEE 86, 2278–2324 (1998)
Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255. IEEE Press, Miami (2009)
LeCun, Y., Boser, B., Denker, J.S., et al.: Handwritten digit recognition with a back-propagation Network. In: Advances in Neural Information Processing Systems, pp. 396–404. Morgan Kaufmann Press, San Francisco (1990)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. J. Sci. 313, 504–507 (2006)
Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. J. Atmos. Environ. 32(14/15), 2627–2636 (1998)
Huang, Y.M., Lin, T., Cheng, S.C.: Effectiveness of a mobile plant learning system in a science curriculum in Taiwanese elementary education. J. Comput. Educ. 54(1), 47–58 (2010)
Lee, S.H., Chan, C.S., Wilkin, P., et al.: Deep-plant: plant identification with convolutional neural networks. In: IEEE Image Processing (ICIP), pp. 452–456. IEEE Press (2015)
Acknowledgments
This work is supported by the Fundamental Research Funds for the Central Universities (2015ZCQ-YS-02) and Beijing Higher Education Young Elite Teacher Project (YETP0785).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Han, JH., Jin, C., Wu, LS. (2020). Research on Accuracy of Flower Recognition Application Based on Convolutional Neural Network. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-20454-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20453-2
Online ISBN: 978-3-030-20454-9
eBook Packages: EngineeringEngineering (R0)