Automatic grading of Bi-colored apples by multispectral machine vision

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.