Machine vision based quality evaluation of chrysanthemum cut flower

Abstract

Cut flower evaluation has been usually conducted by human sense and its criteria are uncertain and subjective. In this paper, machine vision based quality evaluation was done using neural networks to quantify the ambiguous criteria. As input parameters of neural networks, cut flower length, stem diameter, leaf area, and etc. were selected, while human evaluation score was used for an output parameter. The neural networks were trained by KNT method. From the results, it was observed that output value satisfactorily agreed the human evaluation score. The error was less than the human error resulted from the human double check procedure.