Stem-Calyx Recognition of an Apple using Shape Descriptors
This addresses a domain-specific issue in agricultural image processing for apple grading, but it is incremental as it builds on existing methods with new shape features.
The paper tackles the problem of apple grading by recognizing stem-calyx regions to differentiate them from true defects, using shape descriptors and SVM classification, resulting in considerable improvement over existing techniques.
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.