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

作者：van Dael M^*; Lebotsa S; Herremans E; Verboven P; Sijbers J; Opara U L; Cronje P J; Nicolai B M

来源：Postharvest Biology and Technology, 2016, 112: 205-214.

DOI：10.1016/j.postharvbio.2015.09.020

• Endoxerosis
• Granulation
• Defect fruit
• Inspection
• Defects
• Citrus

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 naive 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.

• 出版日期2016-2