Abstract
Tropical rainforest has more than 3,000 different types of timber species, out of these, about 200 species are being used by the timber industry. The properties of these bamboo species varies a lot and different species are recommended for different purpose. Due to this fact, recognition of bamboo species is necessary before its efficient utilization. Due to unavailability of the database ,the databse is developed in Forest Research institute Dehradun by collecting the raw samples of Culm sheath. The three moment based classification techniques ie. Central moment Legendre moment and Fourier moment is adopted to perform the experiment. The performqance of these techniques is measured by introducing three paprameter i.e classwise classification accuracy,overall classifier accuracy and computation time. A confusion matrix is created to quantify the class wise and classifier accuracy. The results show that the Fourier Moment has to be superior classification accuracy compared to Legendre and central moment, and computation time is very low,because only boundary points are consider for calculating the moment.
This application can eliminate the need for laborious human recognition method requiring a plant taxonomist. The results obtained shows considerable recognition accuracy proving that the techniques used is suitable to be implemented for commercial purposes.
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Singh, K., Singh, S. (2012). Comparisons of Three Classifier for Classification of Bamboo Plant. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_91
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DOI: https://doi.org/10.1007/978-3-642-27443-5_91
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