Surface defect detection and classification in mandarin fruits using fuzzy image thresholding, binary wavelet transform and linear classifier model

Machine vision systems with effective image processing methods are used in quality grading of agricultural products. A pattern recognition technique was developed to detect and classify surface defects such as pitting, splitting and stem-end rot found in images of mandarin fruits. The developed technique employs fuzzy thresholding for image segmentation, binary wavelet transform (BWT) for feature extraction and a rule based linear classifier model for detection and classification of the defects. The moment invariants computed from the detail subimage of BWT were taken as feature values. This paper in detail describes about the pattern recognition algorithm and its implementation. The detection and classification results obtained from the algorithm are reported and discussed.