Click on “Download PDF” for the PDF version or on the title for the HTML version.
If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.
Identifying Apple Surface Defects Using Principal Components Analysis and Artificial Neural Networks
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 50(6): 2257-2265. (doi: 10.13031/2013.24078) @2007
Authors: B. S. Bennedsen D. L. Peterson, A. Tabb
Keywords: Apple, Defects detection, Image processing, Neural networks, Sorting
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.(Download PDF) (Export to EndNotes)