Abstract
For the needs of the plant classification, the BP neutral network identification method has been researched based on the characters of the plant leaves veins in this paper. Firstly, the plant leaves image which achieved by the digital device is needed to be pretreated. Secondly, the leaves veins should be extracted by the Canny edge detection, and we use the leaves veins’ binary image to figure out the seven Hu invariant moment. Finally, the seven invariant moment is used to be the input of the BP neutral network to classify the plant leaves. The arithmetic is work out under the integrated environment of MATLAB, and the test result shows that the correct identification rate of the five different plant leaves’ types could reach 96.67%. The identification method is high in rate of correction rate and is easy to implement. It not only can be widely used in the classification research of the plant, but also could improve the accuracy of the plant classification.
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References
Chen, Y.: Automatic plant leaves classification based on leaf shape and vein structure. Master Dissertation, Zhejiang Sci-Tech University, China (2013)
Zhang, L.: The research of computer-aided plant species identification based of leaf feature. Master Dissertation, Northeast Normal University, China (2007)
Zhai, C., Wang, Q., Du, J.: Plant leaf recognition method based on fractal dimension feature of outline and venation. Comput. Sci. 2, 41 (2014)
Dave, S., Runtz, K.: Image processing methods for identifying species of plants. IEEE Wescanex: Communications, Power and Computing, Winnipeg, Canada 15–16 May 1995
Neto, J.C., Meyer, G.E., Jones, D.D., Samal, A.K.: Plant species identification using elliptic Fourier leaf shape analysis. Comput. Electron. Agric. 2, 50 (2006)
Qi, H., Shou, T., Jin, S.: Leaf characteristics-based computer-aided plant identification model. J. Zhejiang For. Coll. 3, 20 (2003)
Qi, H., Yang, J.: Sawtooth feature extraction of leaf edge based on support vector machine. Proceedings of the International Conference on Machine Learning and Cybernetics, Xi’an, China, 2–5 November 2003
Qi, H.: Automatically obtaining of apearance features and computer-aided plant classification and identification. J. Zhejiang For. Coll. 2, 21 (2004)
Wang, X., Huang, D., Du, J., Zhang, G.: Feature extraction and recognition for leaf images. Comput. Eng. Appl. 3, 42 (2006)
Tian, H., Shi, S., Zong, X.: Pattern recognition based on moment invariant feature and BP neural network for image. J. Hebei Univ. Nat. Sci. Ed. 2, 28 (2008)
Xia, Z., Wang, X., Sun, X., Liu, Q., Xiong, Naixue: Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed. Tools Appl. 75(4), 1947–1962 (2016). https://doi.org/10.1007/s11042-014-2381-8
Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X., Ren, K.: A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inf. Forensics Secur. 11(11), 2594–2608 (2016). https://doi.org/10.1109/TIFS.2016.2590944
Zhou, Z., Wang, Y., Wu, Q.M.J., Yang, C.-N., Sun, X: Effective and efficient global context verification for image copy detection. IEEE Trans. Inf. Forensics Secur., 12(1):48–63 (2017). https://doi.org/10.1109/TIFS.2016.2601065, 2016
Acknowledgements
This study was supported by National Natural Science Foundation of China (Nos. 11373075, 61379117, 61771150), Hunan Provincial Natural Science Foundation of China (No. 2015JJ2016), Guangxi Colleges and Universities Key Laboratory of Satellite Navigation and Position Sensing, Youth Talent Support Plan and Scientific Research Foundation of Changsha University (No. SF1615).
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Huang, F., Zhang, J., Shan, Q. et al. The research of the plant leaves identification method based on 3-layers BP neutral network. Cluster Comput 22 (Suppl 5), 11143–11152 (2019). https://doi.org/10.1007/s10586-017-1336-z
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DOI: https://doi.org/10.1007/s10586-017-1336-z