基于三维点云的叶面积估算方法

为实现低成本无损精确测定叶片面积，基于运动恢复结构算法获取点云，提出了一种融合叶片点云分割、表面重建及叶片面积无损估测等过程的植物叶片面积提取方法。首先，基于运动结构恢复算法，以智能手机获取的可见光图像重建植物的三维点云；其次，为了还原叶片表面形状，基于HSV颜色空间，使用阈值分割法去除叶片点云的噪点；使用K-means聚类算法对点云的三维坐标矩阵进行分类，实现单片叶片点云的分割；基于滚球算法重建叶片的表面网格模型；最后，通过计算网格面积求得叶片面积。与常规叶面积测定方法进行了对比，本文方法的计算结果与扫描叶片法测定值相比平均误差为1.21cm2，误差占叶片面积的平均百分比为4.67%；与叶形纸称量法测定值相比平均误差为1.41cm2，误差占叶片面积的平均百分比为6.05%。结果表明，本文方法成本低、精确度高，可满足植物叶片面积无损精确测定的需求。

Leaf Area Estimation Method Based on Three-dimensional Point Cloud

In the study of 3D plant phenotyping, with the efficiency of plant phenotype improving, 3D reconstruction technology has become a novel direction to obtain blade morphology at present. However, the traditional method of reconstructing 3D model such as laser scanner, structured light image and binocular stereo vision system is expensive and complicated. A new method extracting plant leaf area was proposed based on point cloud obtained by using structure from motion. Smart phone was used to get images of plants. Based on these images, the three dimensional point cloud of plants was reconstructed by using structure from motion algorithm. In order to restore the surface shape of the leaf, firstly, the noise of the leaf point cloud was removed by using threshold segmentation algorithm based on HSV color space. Secondly, the three dimensional coordinate matrix of point cloud was classified by using K means clustering algorithm to segment single leaf point cloud by classifying. And then, the surface mesh model of the leaf was reconstructed by using the ball pivoting algorithm. At last, the leaf area was obtained by calculating the mesh area. To evaluate the proposed method, it was compared with the conventional leaf area measurement method. The average error of the calculation result of the proposed method was 4.67% compared with the measured value by using the scanning method, and the average error was 6.05% compared with the measured value by using the leaf shape paper weighing method. In addition, the method of calculating leaf area by extracting contour by Canny edge detection algorithm was compared with the proposed method. The results showed that the proposed method required low cost and high precision, and met the requirements of non destructive and accurate determination of plant leaf area.