Maize Point Cloud Data Filtering Algorithm Based on Vehicle 3D LiDAR

被引：0

作者：

Zhang M. ^{[1
]}

Miao Y. ^{[1
]}

Qiu R. ^{[1
]}

Ji Y. ^{[1
]}

Li H. ^{[2
]}

Li M. ^{[1
]}

机构：

[1] Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing

[2] Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷 / 04期

关键词：

Filtering; LiDAR; Maize; Phenotyping; Point cloud;

D O I：

10.6041/j.issn.1000-1298.2019.04.019

中图分类号：

学科分类号：

摘要：

In order to support phenotypic parameter measurement and digital plant related research, the obtained maize point cloud data collected by 3D light detection and ranging (LiDAR) were analyzed and processed. The filtering algorithm of maize point cloud data was carried out, and a two times filtering algorithm based on statistical analysis was proposed. The vegetative stages of the 12th leaf, Jingnongke 728 and Nongda 84 maize were used as research objects, and VLP-16 was used to collect field maize point cloud data. Firstly, the point cloud data was subjected to pass filtering processing to remove extraneous points. The number of point clouds was reduced from 12 000 to 1 700. Secondly, the point cloud data was subjected to the first filtered process, and the precision and recall threshold were set. The average number of point clouds was reduced from 1 700 to 1 400, and 300 outliers were removed. Then, the point cloud was subjected to the second filtered process. The optimal combination and marginal combinations of precision and recall were determined. The optimal combination was (110, 0.9) and (6, 1.2). The marginal combinations were (100, 1.0), (6, 1.2) and (110, 0.8), (5, 0.9), a total of three combinations of parameters. The average number of point clouds was reduced from 1 400 to 1 300, and 100 outliers were removed. Finally, the three sets of verification set data were tested. The results showed that the optimal combination performance was optimal, which can be used to Jingnongke 728 and Nongda 84. © 2019, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：170 / 178

页数：8

共 27 条

[1] Qiu R., Zhang M., Wei S., Et al., Method for measurement of maize stem diameters based on RGB-D camera, Transactions of the CSAE, 33, pp. 170-176, (2017)
[2] Grobetakinsky D.K., Svensgaard J., Christensen S., Et al., Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap, Journal of Experimental Botany, 66, 18, pp. 5429-5440, (2015)
[3] Zhao C., Lu S., Guo X., Et al., Exploration of digital plant and its technology system, Scientia Agricultural Sinica, 43, 10, pp. 2023-2030, (2010)
[4] Wang C., Zhao M., Yan J., Et al., Three-dimensional reconstruction of maize leaves based on binocular stereovision system, Transactions of the CSAE, 26, 4, pp. 198-202, (2010)
[5] Wang C., Guo X., Wu S., Et al., Three dimensional reconstruction of maize ear based on computer vision, Transactions of the Chinese Society for Agricultural Machinery, 45, 9, pp. 274-279, (2014)
[6] Han D., Yang G., Yang H., Et al., Three dimensional information extraction from maize tassel based on stereoscopic vision, Transactions of the CSAE, 34, 11, pp. 166-173, (2018)
[7] Song Y., Glasbey C.A., Polder G., Et al., Non-destructive automatic leaf area measurements by combining stereo and time-of-flight images, IET Computer Vision, 8, 5, pp. 391-403, (2014)
[8] Qiu R., Miao Y., Ji Y., Et al., Measurement of individual maize height based on RGB-D camera, Transactions of the Chinese Society for Agricultural Machinery, 48, pp. 211-219, (2017)
[9] He D., Shao X., Wang D., Et al., Denoising method of 3-D point cloud data of plants obtained by Kinect, Transactions of the Chinese Society for Agricultural Machinery, 47, 1, pp. 331-336, (2016)
[10] Xia C., Shi Y., Yin W., Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor, Transactions of the CSAE, 34, 6, pp. 168-174, (2018)

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