期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：9
DOI：10.14569/IJACSA.2018.090940
出版社：Science and Information Society (SAI)
摘要：The present work involves statistically analyzing and studying the overall classification accuracy results using Hue channel images of different plant species using their dorsal and ventral sides, and then subjecting them to the process of feature extraction using first order statistical features and texture based features. These extracted features have been subjected to the classification process using KNN and Random Forest algorithms. Further, this work studies the fusion of two different kinds of features extracted for dorsal and ventral plant leaf images and studying the effect of fusion on the overall classification accuracy results. This work also delves into the feature selection task using random forest algorithm and studies the effect of reduced dataset with unique features on the overall classification accuracy results. The most important outcome of this investigation is that the ventral leaf images can be a suitable alternative for plant species classification using digital images and further, the fusion of features does improve the classification accuracy results.
关键词：Dorsal; ventral; leaf classification; random forest; texture features; statistical features
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