基于机器视觉的嫁接用苗外观特征自动检测

提出了基于数学模型的幼苗外观特征自动检测方法,检测项目包括生长状态、子叶参数和胚轴参数。首先经过图像预处理提取幼苗二值图,利用行像素统计图确定特征参数基准点位置。然后以标定胚轴最小矩形倾斜度和宽度判定弯曲状态;子叶跨度通过两子叶端点距离确定,子叶展开角通过两子叶底端平展位置拟合线夹角判定;胚轴弯曲度通过胚轴中心线上曲率最大的位置为分界点分别判断两段斜度而求得,胚轴长、轴径结合斜度补偿求得。与手工测量数据对比,轴长、轴径和子叶跨度的相关系数分别为0.935 1、0.899 9和0.903 4,相对误差分别小于7%、5%和7%,绝对误差分别小于4 mm、0.2 mm和6 mm。

Automatic Detection for External Features of Grafting Seedlings Based on Machine Vision

An automatic detection method for external features of grafting seedlings based on mathematical modeling was studied. The detecting items included growth status (straight or curved, bending direction), cotyledons parameters (cotyledon flare angle, cotyledons flare spans), hypocotyls parameters (curvature, hypocotyl length, hypocotyl shaft), and other external parameters. First, image preprocessing was used to extract the binary image. Then, a reference point and its position were determined by statistic of horizontal pixels. Next, growth status was decided by a set inclination angle of the minimal bounding rectangle of the hypocotyl and its width. After that, the cotyledons span was calculated by the distance of the two cotyledons endpoint, and the cotyledons angle was computed by the angle between two lines that fitting with the bottom of flat cotyledons. Finally, the stem length and the coarse strains were obtained by doing slope compensations to two sections of stem separately which was divided at the point with maximum curvature. Results were compared with manually measured data, and shown that the coefficients of plant height, plant coarse, and cotyledon span were 0.9351, 0.8999 and 0.9034, respectively. And the relative errors of them were less than 7%, 5% and 7%, while the absolute errors of them were less than 4 mm, 0.2mm and 6mm, respectively.