农业工程学报Issue(24)：196-203,8.DOI:10.3969/j.issn.1002-6819.2013.24.026
基于机器视觉的红掌检测分级方法
Determination and grading of Anthurium based on machine vision
摘要
Abstract
With the raising of our consumption, the sales of flowers keep on expanding. Anthurium, as a prominent example, is facing some problems. Productivity and identical grading standards are restricted by the traditional method of grading in the actual process of production. These issues affect the quantity and quality of the product in China. In this paper, an online determination and grading method based on machine vision was developed. The variety of test materials was“flame,”which is popular in China. In accordance with the national standard, 30 pots of red anthurium of grade A, 30 pots of grade B and 30 pots of grade C, a total of 90 pots, were chosen to be test materials. After integrating national standards, the Ministry of Agriculture standards, and local standards about flowering potted plant, new standards were drawn out which are suitable for machine vision grading. In the standards, height, crown diameter, spathe quantity, and spathe diameter were to be the four items to be determined for every potted red anthurium. A horizontal camera and a vertical camera were used in this method to capture the side view and top view photos of 90 Anthurium one by one at the same time. After binarization and edge-based segmentation, the side contour, top contour, and spathe contour were extracted out. The side view photo was scanned in, the first point of side contour was obtained, and this point was regarded as the top point of this plant. The height could be calculated out through the coordinate of the top point. The result obtained by the traditional method, measured with a tapeline, was taken as the normal height. Experimental results show that the measurement error of height was less than 5.4 mm by the machine vision method. To reflect the growth state of the plant more accurately, this paper put forward an equivalent diameter method to use instead of crown diameter. In the top view photo, the crown area of each red anthurium was drawn out from the top contour, the top contour was transformed to an equivalent circle with the same area;and the diameter of equivalent circle was the equivalent diameter of the crown. There always may be some empty holes or gaps between leaves for a big-leaf plant in the top view photo. Therefore, the equivalent diameter method can avoid the effect of the empty holes and gap on the growth state of the plant. This method is more precise than using crown diameter. To count out the number of spathe in one red anthurium, an identification model of spathe was trained, which is complete and symmetry, and arranged all the identified spathe by similarity rate with the identification model from spathe contour. The result of this experiment showed that the correct rate for counting was up to 100%for the 90 pots red anthurium. Taking certain spathe with the highest similarity rate as the target, the widest diameter that is perpendicular with the main nervure was measured. Taking the result by traditional method, measurement was obtained with a tapeline as the normal spathe diameter. The maximum relative error of the spathe diameter was 6.5%. The result of the validate experiment showed that the success rate was up to 97.8%for grading 90 pots of red anthurium. This means that the method based on machine vision fulfilled the objective, which is online determination and grading for potted red anthurium.关键词
机器视觉/图像分割/分级/规模化/盆花/红掌Key words
computer vision/image segmentation/grading/flowering potted plants/red anthurium分类
信息技术与安全科学引用本文复制引用
李恺,杨艳丽,刘凯,辜松,张青,赵亮..基于机器视觉的红掌检测分级方法[J].农业工程学报,2013,(24):196-203,8.基金项目
国家“863”计划资助项目 （）
