Anthurium is the second largest flower export in Taiwan. Since 2001, about 3161 metric tons of anthurium cut flowers have been exported to various countries around the world, mainly Japan, and creating over 18 million US dollars revenue for Taiwan’s flower and plant industries. Before each anthurium cut flower is exported, it is graded for size and sheathed with plastic bag. It must also be free of cuts and bruises, and cannot contain any surface defects. At the current stage, anthurium cut flower grading and inspection is performed manually, which is very time consuming and inconsistent. Therefore, there is a need for an objective and automatic grading and inspection system for anthurium cut flowers. We propose a system that uses machine vision algorithm for grading of anthurium cut flowers. Color image segmentation and blob analysis techniques are used to measure the spathe width of anthurium cut flowers and the measurements are then used to grade the flowers according to official grading standard. A cut detection algorithm and a surface defect classification scheme are also proposed to facilitate the detection of cuts and surface detects on the flowers. An automatic discharging mechanism is designed and added at the backend of the system thus making the grading system fully automatic. Of the 100 flower samples used in the off-line experiment, the average measurement error of spathe width for the machine vision system against manual measurement is 2.3%. The overall accuracy of the cut detection algorithm on the 100 sample is 97%. The surface defect classification algorithm is able to identify all of the defect samples but reports 14 false detections on the good samples, resulting in an overall accuracy of 86%. In the on-line experiment, the grading accuracy of the machine vision system is 98%, and the success rate of the discharging mechanism is 92% based on 50 discharge operations.
