Title
A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Subject
Physics
Biology
Pharmacology. Therapy
Source (journal)
Postharvest biology and technology: an international journal
Volume/pages
112(2016) , p. 205-214
ISSN
0925-5214
ISI
000371935900023
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Oranges and lemons can be affected by the physiological disorders granulation and endoxerosis respectively, decreasing their commercial value. X-ray radiographs provide images of the internal structure of citrus on which the disorders can be discerned. An image processing algorithm is proposed to detect these disorders on X-ray projection images and classify samples as being affected or not. The method automatically segments healthy and affected tissue, calculates a set of image features and uses these to classify the images using a naïve Bayes or kNN classifier. The developed method avoids the need for labour-intensive destructive sampling and allows for non-destructive inspection of all fruits while preventing losses due to destructive sampling. The proposed algorithm classifies 95.7% of oranges and 93.6% of lemons correctly. The classification method is fast, robust to noise and can be applied to any existing inline X-ray radiograph equipment.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/c64d0a/128227.pdf
E-info
https://repository.uantwerpen.be/docman/iruaauth/384c41/128227.pdf
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