Abstract
A grading method of potted Anthurium based on machine vision is proposed. A detection system is designed to acquire color images and depth images of potted Anthurium, and the three-dimensional point-cloud image is reconstructed after registration. According to the testing requirements of potted Anthurium, the minimum enclosing rectangle method is used to measure the width of crowns and spathes. The bubble sequencing method is used to measure the plant height, and the clustering segmentation method is used to calculate the number of spathes. Online automatic grading software for potted Anthurium is developed. Compared with manual measurement, the average measurement accuracies of machine vision for crown width, plant height, spathe width, and spathe number are 98.4%, 98.4%, 98.8%, and 86.7%, respectively. The accuracy rate of grading is 85.86%, which can meet the requirements of automatic grading of potted Anthurium.
Highlights
With the change of consumption fashion and the improvement of purchasing power, people’s demand for flowers is increasing
According to the standards GB/T18247.2-2000, DB44/T154-2003, and NY/T1656.32008 [30–32], potted Anthurium can be divided into two varieties: small flower and large flower, and each variety can be divided into three grades. e grading basis is shown in Tables 1 and 2, respectively. e lowest grade of all evaluation parameters is taken as the grade of potted Anthurium, and potted Anthurium lower than the third level is removed directly
The smallest z-point machine vision and manual measurement methods were used for grading experiments, and the measurement and grading results were compared and analyzed. e experimental samples were 92 large variety potted Anthurium purchased from the Guangdong Academy of Agricultural Sciences, China
Summary
With the change of consumption fashion and the improvement of purchasing power, people’s demand for flowers is increasing. Potted Anthurium, a highly ornamental tropical flower, has gained in popularity in recent years. Grading is an important part of large-scale production of potted flowers. E traditional manual method is time-consuming and laborious, lacks objective consistency, and may cause damage to flowers, in addition to being difficult to adapt to current large-scale production. Automated equipment is urgently needed to replace manual grading. Nondestructive testing technology based on machine vision has become an inevitable trend of flower grading due to its good objective consistency and high efficiency [1,2,3,4,5,6,7,8,9,10]
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