Automatic Defect Segmentation of ‘Jonagold’ Apples on Multi-Spectral Images: A Comparative Study

Abstract

In this work, several thresholding and classification-based techniques were employed for pixel-wise segmentation of surface defects of ‘Jonagold ’ apples. Observations showed that segmentation by supervised classifiers was more accurate than the rest. Also, average of class-specific recognition errors was more reliable than error measures based on defect size or global recognition. Segmentation accuracy im-proved when pixels were represented as a neighborhood. Effect of down-sampling on segmentation accuracy and computation times showed that multi-layer percep-trons were the best. Russet was the most difficult defect to segment, whereas flesh damage the least. The proposed method was much more precise on healthy fruit