Stem-Calyx Recognition of an Apple using Shape Descriptors

TL;DR

This paper introduces a shape descriptor-based method for accurately recognizing apple stem-calyx regions, improving apple grading by distinguishing these from defects using segmentation, feature extraction, and SVM classification.

Contribution

A novel approach combining shape descriptors and SVM to differentiate stem-calyx from defects, enhancing apple grading accuracy.

Findings

Significant improvement in stem-calyx recognition accuracy

Effective segmentation and feature extraction methods

Better differentiation from defects compared to existing techniques

Abstract

This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects, this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and small defects are detected using multi-threshold segmentation. The shape features are extracted from detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated using experiments conducted on apple image dataset and results exhibit considerable improvement in recognition of stem-calyx region compared to other techniques.