A Real-Time Grading Method Of Apples Based On Features Extracted From Defects

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.