A real-time grading method of apples based on features extracted from defects
摘要：
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.
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DOI：
10.1016/S0260-8774(03)00189-4
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年份：
2004
Elsevier
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掌桥科研
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