Skip to main content
padlock icon - secure page this page is secure

Apple Defects Detection Using Principal Component Features of Multispectral Reflectance Imaging

Buy Article:

$107.19 + tax (Refund Policy)

Apple defects detection using hyperspectral imaging has become an active research topic during the last decade. The main merit of hyperspectral imaging is that it has a lot of information, but its data size is big. In the hyperspectral imaging, the most challenging aspect is to reduce the data size while keeping the vital information. Small data size is an essential component for real-time processing that the industries need. The methods to reduce the data size for hyperspectral imaging are generally statistical methods. In this paper, the statistical data reducing method is specialized for apple hyperspectral image data. This paper proposes an apple defects detection using multispectral imaging and principal component analysis. In the preprocessing, we examine the image quality for all the hyperspectral apple image in the spectral range from 403 to 988 nm and select three wavelengths. In the main processing, we perform principal component analysis for the three wavelengths and choose the best principal components for apple defects detection. And the defected apples are detected sequentially using the principal components and global thresholds. We show the algorithm for the above processing and an experiment with hyperspectral apple images. Preliminary examination shows that the general detection rate is 97%.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics


Document Type: Research Article

Publication date: July 1, 2018

More about this publication?
  • Science of Advanced Materials (SAM) is an interdisciplinary peer-reviewed journal consolidating research activities in all aspects of advanced materials in the fields of science, engineering and medicine into a single and unique reference source. SAM provides the means for materials scientists, chemists, physicists, biologists, engineers, ceramicists, metallurgists, theoreticians and technocrats to publish original research articles as reviews with author's photo and short biography, full research articles and communications of important new scientific and technological findings, encompassing the fundamental and applied research in all latest aspects of advanced materials.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more