کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4517939 | 1624982 | 2016 | 10 صفحه PDF | دانلود رایگان |
• CT scans of oranges and lemons with expected high degrees of defects were taken.
• Radiographs were simulated to take random fruit orientations into account.
• An image processing algorithm to detect defects on radiographs is proposed.
• Performance of difference classifiers was compared, with and without noise.
• The proposed algorithm classifies 95.7% of oranges and 93.6% of lemons correctly.
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.
Journal: Postharvest Biology and Technology - Volume 112, February 2016, Pages 205–214