Apple color grading based on organization feature parameters

This paper presents a system for apple color grading into four classes according to standards stipulated in China. To automatically grade apple fruit color, a laboratory machine vision system was developed, which consisted of a color CCD camera equipped with an image grab device, a bi-cone roller device controlled by a stepping motor, and a lighting source. Four images, one for every rotation of 90^o, were taken from each apple. Seventeen color feature parameters (FP) were extracted from each apple in the image processing. Three hundred and eighteen ''Fuji'' apples were examined by the system, and were divided into two sets, with 200 in ''Training set'' and 118 in ''Test set''. A method called organization feature parameter (OFP), based on formulae expression trees by using genetic algorithms (GA), was used in this paper. When the initial FP could not sensitively distinguish among different classes of apples, the FP were organized into one new OFP by using genetic algorithm. By applying the step decision tree algorithm in combination with the OFP method, high grade judgment ratios were achieved in the classification of two of four apple color grades, i.e., 'Extra', and 'Reject'. However, the grade judgment ratio for 'class I' and 'class II' was relatively low. Compared with BP-ANN and SVM, the OFPs method was more accurate than BP-ANN, but a little lower than SVM for identification results.