Authors

Abstract

Aiming at the problems of low precision, strong subjectivity, and continuous measurement in the current measurement methods of corn phenotypic traits, a method of measuring corn phenotypic traits with high precision, low cost, easy carrying and continuous measurement was proposed. Firstly, the three-dimensional scanning device Kinect 2.0 is used to collect corn information and process and reconstruct the collected point cloud. Then, the stem and leaf point clouds were segmented by straight-through filtering, ellipse fitting and region growth segmentation. Finally, the phenotypic parameters of corn were obtained by triangulation and plane fitting for the segmented corn leaves, and the accuracy was analyzed. The results showed that the accuracy of corn plant height was 97.622 %, the average relative error of stem long axis was 9.46 %, the average relative error of stem short axis was 11.17 %, and the accuracy of leaf area was 95.577 %. Studies have shown that this method provides a new method for continuous measurement of phenotypic traits in corn.

Abstract in Chinese

针对目前玉米表型性状测量方法存在精度低，主观性强，不能连续性测量且采集设备价格昂贵且不方便携带等问题，提出一种精确度高、耗费少、便携带且可连续性测量的玉米表型性状测量方法。首先，采用三维扫描设备Kinect 2.0采集玉米信息并对采集后的点云进行处理和三维重建。然后，利用直通滤波、椭圆拟合、区域增长分割方法分割出玉米茎秆叶片等数据。最后，对分割后的玉米叶片等利用三角面片化以及平面拟合等方法获取玉米植株表型参数并对其进行精度分析。结果表明：算法测量株高精确度为97.622%，茎秆长轴的平均测量相对误差为9.46%，茎秆短轴平均测量相对误差为11.17%，叶片面积精确度为95.577%。研究表明，本文方法为玉米的表型性状测量提供一种连续性测量新方法。