Identifying Apple Surface Defects Using Principal Components Analysis and Artificial Neural Networks

Abstract

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components, derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). In an iterative process, different ways of preprocessing images prior to training the networks were attempted. Best results were obtained by removing the background and applying a Wiener filter to the images. Overall, the best performance obtained was 79% of the defects detected in a test set consisting of 185 defects.