A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs

© 2015 Elsevier B.V. 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.publisher: Elsevier articletitle: A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs journaltitle: Postharvest Biology and Technology articlelink: http://dx.doi.org/10.1016/j.postharvbio.2015.09.020 content_type: article copyright: Copyright © 2015 Elsevier B.V. All rights reserved.status: publishe