Publication
Title
A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs
Author
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.
Language
English
Source (journal)
Postharvest biology and technology: an international journal. - -
Publication
2016
ISSN
0925-5214
Volume/pages
112(2016), p. 205-214
ISI
000371935900023
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
PICKNPACK: Flexible robotic systems for automated adaptive packaging of fresh and processed food products
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 19.10.2015
Last edited 03.12.2017
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