A method for calculating the leaf inclination of soybean canopy based on 3D point clouds. Issue 15 (3rd August 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhichao; Ma, Xiaodan; Guan, Haiou; Zhu, Kexin; Feng, Jiarui; Yu, Song
• Is Part Of: International journal of remote sensing. Volume 42:Issue 15(2021)
• Page Start: 5721
• Page End: 5742
• Publication Date: 2021-08-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1930271
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: In order to achieve high-efficiency, low-cost and non-destructive measurement of soybean leaf inclination, three soybean varieties (Fudou-6, Kangxian-9 and Kangxian-13) were taken as research objects, a calculation method of soybean leaf inclination based on 3D point clouds was proposed. First, the original 3D point cloud data of soybean plants were obtained by Kinect 2.0 depth camera. Second, the grid method, depth threshold filtering and statistical filtering were used to pre-process the original 3D point cloud data. Third, the k -means clustering algorithm was used to segment the leaf point clouds. Further, the Delaunay triangulation was applied in reconstructing the surface of discrete point clouds. Finally, the ratio of leaf area to projected area was obtained by calculating the area of triangular mesh, so as to realize the calculation of soybean leaf inclination. At the same time, the calculated values were compared with those obtained by multispectral three-dimensional laser scanning device. The average relative error of soybean leaf inclination was 3.21%. The coefficient of determination ( R 2 ) of the three varieties were 0.8317, 0.9075 and 0.9186, respectively. The results showed that the proposed method could meet the needs of non-destructive and accurate measurement of soybean leaf inclination.