Abstract
This paper is presenting here the plant classification method using imaging technology which is useful for classifying the plants by providing the leaf image as an input. The proposed method performs classification by automatically extracting shape patterns and features performing the image processing techniques and neural network model. This method gets the leaf image as an input, performs the leaf image processing tasks, and automatically extracts the leaf shape pattern and leaf shape features. It performs the classification with the help of leaf shape features using neural network techniques. This method presents plant classification using the leaf shape features and feed forward back propagation neural network model. This method results in up to 99% accuracy of classification. This method extracts the leaf shape features and patterns automatically using image processing techniques.
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References
Amlekar, M., Gaikwad, A., Yannawar, P., & Manza, R. (2015). Leaf shape extraction for plant classification. In IEEE International Conference on Pervasive Computing (ICPC).
Beghin, T., Cope, J. S., Remagnino, P., & Barman, S. (2010). Shape and texture based plant leaf classification. ACIVS, 2, 345–353.
Deokar S. R., Zope, P. H., & Suralkar, S. R. (2013, January) Leaf recognition using feature point extraction and artificial neural network. International Journal of Engineering Research & Technology (IJERT), 2(1).
Du, J. X., Wang X. F., & Zhang, G. J. (2007). Leaf shape based plant species recognition. Applied Mathematics and Computation, 185.
Du, J., Huang, D., Wang, X., & Gu, X. (2005). Shape recognition based on radial basis probabilistic neural network and application to plant species identification. In Proceedings of 2005 International Symposium of Neural Networks, ser. LNCS 3497. Berlin: Springer.
Gu, X., Du, J. X., & Wang, X. F. (2005). Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In Proceedings of International Conference on Intelligent Computing 2005, ser. LNCS3644. Berlin: Springer.
Hossain, J., & Amin, M. (2010). Leaf shape identification based plant biometrics. In 13th International Conference on Computer and Information Technology (ICCIT) (pp. 458–463), December 23–25, 2010.
Intelligent Computing Laboratory, Chinese Academy of Sciences Homepage http://www.intelengine.cn/dataset/index.html, Plant leaf image dataset, 2010–2012.
Jiazhi, P, & Yong, H. (2008). Recognition of plant by leaves digital image and neural network. In International Conference on Computer Science and Software Engineering (Vol. 4, pp. 906–910), December 2008.
Lee S. H., Chang, C. S., Mayo, S. J., & Remagnino, P. (2017). How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 71, 1–13.
Suman, S. G, & Deshpande, B. K. (2017, May). Plant leaf classification using artificial neural network classifier. International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 5(5).
Wang, X. F., Du, J. X., & Zhang, G. J. (2005). Recognition of leaf images based on shape features using a hypersphere classifier. In Proceedings of International Conference on Intelligent Computing 2005, ser. LNCS 3644. Berlin: Springer.
Wu, S. G., Bao, F. S., Xu, E. Y., Wang, Y., Chang, Y., & Xiang, Q. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. arXiv-0707.4289V1[CS.AI].
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Amlekar, M.M., Gaikwad, A.T. (2019). Plant Classification Using Image Processing and Neural Network. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-1274-8_29
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DOI: https://doi.org/10.1007/978-981-13-1274-8_29
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