Reference : Automatic grading of Bi-colored apples by multispectral machine vision
Scientific journals : Article
Life sciences : Agriculture & agronomy
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/81208
Automatic grading of Bi-colored apples by multispectral machine vision
English
Unay, Devrim [> >]
Gosselin, Bernard [> >]
Kleynen, Olivier [> >]
Leemans, Vincent mailto [Université de Liège - ULg > Sciences et technologie de l'environnement > Mécanique et construction >]
Destain, Marie-France mailto [Université de Liège - ULg > Sciences et technologie de l'environnement > Mécanique et construction >]
Debeir, Olivier [> >]
2011
Computers & Electronics in Agriculture
Elsevier Science
Yes (verified by ORBi)
International
0168-1699
[en] Fruit grading ; Defect detection ; Multispectral images ; Feature extraction ; Feature selection ; Classification
[en] In this paper we present a novel application work for grading of apple fruits by machine vision. Following
precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images,
statistical, textural and geometric features are extracted from the segmented area. Using these features,
statistical and syntactical classifiers are trained for two- and multi-category grading of the fruits. Results
showed that feature selection provided improved performance by retaining only the important features,
and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art,
our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work
we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally
less demanding, while the cascaded one is more accurate.
Région wallonne : Direction générale des Technologies, de la Recherche et de l'Energie - DGTRE
Researchers
http://hdl.handle.net/2268/81208

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