Leaf Recognition using Texture Features for Herbal Plant Identification

Abstract

This research investigates the application of texture features for leaf recognition for herbal plant identification.  Malaysia is rich with herbal plants but not many people can identify them and know about their uses.   Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary.  Leaf image is chosen for plant recognition since it is available and visible all the time.   Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections.  A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier.  A new leaf dataset has been constructed from ten different herb plants.  Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.