GLCM Features And Hybrid Manhattan Distance With SVM For Identification Of The Leaves Of Indonesian Herbal Medicines
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Abstract
Herbal plants are those plants, which can be considered as a substitute for naturally healing diseases. The public still does not have the awareness about the presence of herbal plants. It is owing to the fact that there are several kinds of medicinal plants available and therefore, specialized knowledge is required for their identification. To get over this, an intelligent and precise herbal leaf recognition system is necessary. In recent work introduces one enhanced model for Indonesian Herbal Medicines identification. In which there are three techniques introduced. The first one includes the novel leaf feature framework. There are 16 distances between perimeter points and leaf centroid points, as well as seven median line connectors. The second project entails the creation of leaf feature extraction techniques and algorithms that allow for the production of 23 leaf shape attributes for any type of herbal crop. Thirdly, forming a prototype identification system or introducing herbal plants on the basis of morphological features of the leaf. The procedure of recognition is performed with the help of two mechanisms, which include MDs (Manhattan Distances) and Artificial Neural Networks. However in existing work foreground segmentation was not done based on any particular algorithm and its leads to poor segmentation results. Also ANN is about the executing with parallel processing, and therefore processors providing support to parallel processing is required, and therefore the ANNs exhibit hardware dependency.
To avoid those problems in this work introduced an improved model for herbal medicines plant leaf identification. In this pre-processing is done by using Image segmentation based on k means clustering to segment the foreground object, Image Filtering and Translation of RGB (Red, Green, Blue) images into Grayscale. Features are extracted using GLCMs (Grey Level Co-Occurrence Matrices) and Centre point based model. Finally the Indonesian Herbal Medicines plant leafs are identified based on those extracted features using Hybrid MDs with SVMs (Support vector machines). Experimental results shows that this proposed model produces better leaf identification accuracy than other state of the art models.
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References
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