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Study on Detection Method for Cut Roses Based on Machine Vision

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1008777.(doi:10.13031/2013.29725)
Authors:   Jiangbo Li, Xiuqin Rao, Yibin Ying, zhenyu Zhang
Keywords:   Machine vision, Cut roses, Detection, Linear Regression Method.

Recently, research of detection and grading of cut flower is few. In this study, the detection algorithms of stem-length, diameter, curvature and bud opening degree for cut roses were proposed. The stem-length was computed by scanning and searching the highest and lowest point of cut roses. The stem was extracted using morphology method, then the average diameter was obtained by the diameter of the top and bottom. The algorithm based on calculating anti-cosine angle was put forward to estimate curvature, and projected area and length-width ratio ware utilized to measure cut rose opening degree. Finally, a linear regression method was used for developing prediction models of cut rose stem-length. In this research, sixty cut rose samples were used, and the results showed that the coefficient of determination R2 was 0.9848 and the average deviation was 0.61 cm between actual measurement values and experimental detection valves of stem-length.

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