An Efficient Double Labelling Image Segmentation Model for Leaf Pixel Extraction for Medical Plant Detection

Abstract

Medicinal plants have less adverse effects and less expensive than modern medicine in the pharmaceutical industry. On this basis, many researchers have shown significant interest in the research into the recognition of automatic medicinal plants that benefits human health. In order to produce a robust classifier, there are different possibilities of improvement that enable medical plants to be classified correctly in real time. A large number of segmentation processes are used to handle various types of images and implementations. No recognition system can deal effectively with all sorts of images. Common parameters are seen as the basis for segmentation in various pixels. Common parameters here refer to value of intensity, colour and so on. The convex subjects, edges of various complex structures and closed contours are mainly used for these techniques. A pixel is separated independently from its grey values as pixel objects. As pixels of objects, isolated points or small areas are distinguished, because connectedness is not regarded as an essential feature. In this method an original image will be separated into pixels with small adjacent pixels. The pixel size is normally based on the operator's mask size. The object's characteristics are calculated on the object's edges, and the masks often consider the pixels from the background images. In this proposed work, an efficient Double Labelling Image Segmentation Model (DLISM) for leaf pixel extraction for accurate medical plant detection is introduced. The proposed model is contrasted with the traditional models and the results proved that the proposed model exhibits better performance levels.