Abstract:
In image processing, leaf shape recognition requires a huge database of similar images. Normal leaf image database construction requires more time and space. On the other hand, mobile offline applications are not able to contain huge image database with high pixel ratio. To solve this problem, mainly in medicinal plant identification, small leaf descriptors are necessary to completely provide the required plant information. Presently, the Botanists use shape reference table to recognize the following shapes of the leaf: ovate, cordate, elliptical, oblong, lanceolate and linear. As the shape numbers are invariant under scale, rotation and translation, which is highly desirable property for object recognition and chain code techniques preserve data by allowing large data reduction. Hence, in the proposed Shape Descriptor Algorithm for Medicinal Plant Identification (SDAMPI), we developed a descriptor to resolve the pixel selection issue of Freeman chain code and generate a unique leaf shape number. This leaf shape digital descriptor will act as a reference table for medicinal plant leaf shape identification. The performance of the proposed descriptor is evaluated through Jaccard similarity index graph and Levenshtein distance (LD) graph. From the results, it is confirming that, SDAMPI descriptor can detect Medicinal plant leaf shapes more accurately than existing methods.
