Vision based fruit sorting system using measures of fuzziness and degree of matching

Fuzzy approaches were used to determine optimal thresholding values of fruit's images, and fuzzy degree of matching was applied to classify the color and size of fruit. Results showed that fuzzy method was superior to the traditional statistical methods, and a accuracy of 93.3% for combined sorting was reported. The errors due to miscategorization could thus be reduced if the fuzzy methods were used. The developed fuzzy algorithms were integrated with the machine vision guided robotic sorting system for fruits.<
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