摘要
为实时获取温室作物生长形态参数,应用线激光,对作物整体进行非接触式扫描,通过CCD摄像机实时拍摄扫描过程,采用重心法计算激光光条中心,获取作物叶片与茎秆的三维点云信息,实现作物形态三维点云结构重建;提出适用于作物三维点云数据特征的迭代法,提取叶片点云子集的中心轴线,通过曲线拟合计算叶片长度;根据摄像机透视原理,提出针对细小茎秆的静态定位法计算茎秆直径。试验表明,激光视觉量测叶片长度与茎秆直径的准确率分别为95.39%(SE为0.2961,R2=0.916)和94.55%(SE为0.008 7,R2=0.915)。
A laser vision-based measurement system consisting of a camera and a laser sheet that scanned the plant vertically was developed to measure the stem diameters and leaf lengths automatically. The 3D point cloud was obtained with the laser sheet scanning the plant vertically, while the camera videoed the process of laser scanning. Laser line centers were extracted by improved centroid method. The 3D point cloud structure of the sample plant was obtained. For leaf length measurement, iteration method for point clouds was used to extract the axis of the leaf point cloud set. The centroid of the subset of points was calculated and taken as the next axis point. Leaf length was calculated by curve fitting on these axis points. In order to increase the accuracy of curve fitting, bi-directional starting point selection was used. To evaluate the method in a sample of 8 water spinaches, the lengths of leaves and diameters of stems were measured manually and plotted versus their automatically measured counterparts. The accuracy of leaf lengths and stem diameters are 95.39% and 94.55% respectively. The tests proved that laser visionbased method could be used on plant geometries measurement in greenhouses costly and portably.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2014年第9期254-259,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(61273227)
关键词
温室
作物茎叶
形态参数
激光视觉
三维点云
Greenhouse Leaf and stem of plant Morphological parameters Laser vision 3 D point clouds
