Apple Defect Segmentation by Artificial Neural Networks

This paper presents a defect segmentation work for bi-colored apple fruits performed by several artificial neural networks. Pixel-wise classification approach is employed to realize segmentation. Quantitative and qualitative evaluations showed that competitive networks were more erroneous while feed-forward and recurrent networks tested were more accurate in segmenting apple defects.