Automated online detection of granulation in oranges using X-ray radiographs
Oranges can develop granulation during production, a condition in which the juice sacs shrivel because of gel formation. The aim of this work is to develop an image processing algorithm to reliably detect granulation in X-ray projection images or radiographs of oranges. Oranges grown at South-African orchards with a known high incidence of granulation were scanned in an X-ray system (75 kV, 468 mA, 60-ms exposure). Subsequently they were destructively evaluated on the presence of granulation to serve as a ground truth. An image processing algorithm was developed to automatically segment the affected fruit tissue and train a naive Bayes classifier based on the spatially discretized features. The resulting high-speed and robust algorithm can be implemented in existing on-line X-ray radiograph systems. When applied in a sorting line, this should result in sampling ratios close to 100%, with no losses of healthy fruit due to destructive testing. Furthermore the end product will have a guaranteed low presence of granulation, increasing commercial value.
Van Dael, M., Herremans, E., Verboven, V., Opara, U.L., Nicolaï, B. and Lebotsa, S. (2016). Automated online detection of granulation in oranges using X-ray radiographs. Acta Hortic. 1119, 179-182
non-destructive, fruit, internal quality, assessment, defects, citrus