A method to sort ÔJonagoldÕ apples based on the presence of defects was proposed. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed. Multi-spectral images of sound and defective fruits were acquired tending to cover the whole colour variability of this bicolour apple variety. Defects were grouped into four categories: slight defects, more serious defects, defects leading to the rejection of the fruit and recent bruises. Stem-ends/calyxes were detected using a correlation pattern matching algorithm. The efficiency of this method depended on the orientation of the stem-end/calyx according to the optical axis of the camera. Defect segmentation consisted in a pixel classification procedure based on the BayesÕ theorem and non-parametric models of the sound and defective tissue. Fruit classification tests were performed in order to evaluate the efficiency of the proposed method. No error was made on rejected fruits and high classification rates were reached for apples presenting serious defects and recent bruises. Fruits with slight defects presented a more important misclassification rate but those errors fitted however the quality tolerances of the European standard. Considering an actual ratio of sound fruits of 90%, less than 2% of defective fruits were classified into the sound ones.

This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine.These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73 %. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.

Research on the Black Sheep effect (Marques, Yzerbyt, & Leyens, 1988) suggests that motivational factors such as the level of identification with the ingroup influences the way people react against negative ingroup members. The present study tested the idea that people may invest a sizable amount of cognitive resources to protect their view of the ingroup when it is challenged by a negative target. We measured the identification of our participants, all students in psychology, with the larger group of psychologists and presented them with descriptions of four ingroup members, three positive and one negative. As expected, high identifiers gave a harsher judgment of the negative target than did low identifiers. In addition, participants’ performance on a secondary task confirmed that high identifiers devoted more resources than low identifiers to process the information about the negative member as compared to a positive ingroup member. These results stress the relationship between motivation and cognitive resources in general, and the Black Sheep effect and stereotyping in particular.

A method based on colour information is proposed to detect defects on 'Golden Delicious' apples. In a first step, a colour model based on the variability of the normal colour is described. To segment the defects, each pixel of an apple image is compared with the model. If it matches the pixel, it is considered as belonging to healthy tissue, otherwise as a defect. Two other steps refine the segmentation, using either parameters computed on the whole fruit, or values computed locally. Some results are shown and discussed. The algorithm is able to segment a wide range of defects.

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.