Classification and Grading of Arecanut Using Texture Based Block-Wise Local Binary Patterns

Abstract

Arecanut is a commercial crop typical to high rain fall regions. Arecanut has economic, cultural and medicinal importance, and is categorized into different types depend upon the region which grow and market it consumes.In this paper, an attempt towards grading of  Arecanut images is proposed. The proposed approach makes use of global textural feature viz., Local Binary Pattern for feature extraction. Initially, an image is divided into k number of blocks. Subsequently, the texture feature is extracted from each k blocks of the image. The k value is varied and has been fixed empirically. For experimentation purpose, the Arecanut dataset is created using 4 different classes and experimentation is done for whole image and also with different blocks like 2, 4 and 8. Grading of Arecanut is done using Support Vector Machine classifier. Finally, the performance of the grading system is evaluated through metrics like accuracy, precision, recall and F –measure computed from the confusion matrix. The experimental results show that most promising result is obtained for 8 block of the image.