Texture based decision tree classification for Arecanut

Authors:
Ajit Danti;M. Suresha
Affiliations:
J.N.N College of Engineering, Shimoga, Karnataka, India;Kuvempu University, Karnataka, India
Venue:
Proceedings of the CUBE International Information Technology Conference
Year:
2012

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Abstract

Recent work in feature-based classification has focused on non-parametric techniques that can classify samples. Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. Many machine learning systems have been developed for constructing decision trees from collection of examples. So far, grading of arecanut is done by trained experts manually. Currently, no work has been attempted towards automated classification of arecanuts. This paper discusses technique for classification of arecanut based on texture features. Classification is done using Mean around features, Gray level co-occurrence matrix (GLCM) features and combined (Mean around-GLCM) features. Decision trees classifier is used for classification of arecanut in to six classes. Results obtained from the proposed method are well accepted and solutions are good agreement with the agricultural experts. Proposed approach is experimented on large data set using cross validation and found approximately 99.05% success rate.