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Title
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A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Postharvest biology and technology: an international journal. - -
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Publication
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2016
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ISSN
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0925-5214
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DOI
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10.1016/J.POSTHARVBIO.2015.09.020
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Volume/pages
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112
(2016)
, p. 205-214
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ISI
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000371935900023
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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