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.

XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): International Symposia on Abscission Processes in Horticulture and Non-Destructive Assessment of Fruit Attributes

M. Van Dael, E. Herremans, V. Verboven, U.L. Opara, B. Nicolaï, S. Lebotsa

non-destructive, fruit, internal quality, assessment, defects, citrus

https://doi.org/10.17660/ActaHortic.2016.1119.24