Abstract
Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy, such as leaf area, leaf distribution, and 3D model. The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds. This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive, unorganized LiDAR point clouds. In order to mine the distinct geometry of corn leaves and stalk, the Difference of Normal (DoN) method was proposed to extract corn leaf points. Firstly, the normals of corn leaf surface for all points were estimated on multiple scales. Secondly, the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution. Finally, the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points. The quantitative accuracy assessment showed that the overall accuracy was 94.10%, commission error was 5.89%, and omission error was 18.65%. The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive, unorganized terrestrial LiDAR point clouds using the proposed DoN method. Keywords: corn leaves, terrestrial LiDAR, cloud points, automatic extraction, crop growth monitoring, phenotyping, difference of normal (DoN), directional ambiguity of the normals DOI: 10.25165/j.ijabe.20181103.3177 Citation: Su W, Zhang M Z, Liu J M. Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds. Int J Agric & Biol Eng, 2018; 11(3): 166–170.
Highlights
Crop leaves play a significant role for crop plants[1], for example, they determines light interception and plant productivity[2]
In order to mine the distinct geometry of corn leaves and stalk, the Difference of Normal (DoN) method was proposed to extract corn leaf points
The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive, unorganized terrestrial LiDAR point clouds using the proposed DoN method
Summary
Crop leaves play a significant role for crop plants[1], for example, they determines light interception and plant productivity[2]. The estimation of leaf structural and biophysical parameters such as leaf area[3], leaf morphology, leaf structure, leaf chlorophyll content etc., is very important for crop growth monitoring[4]. The estimation of these structural and biophysical parameters for corn leaf is very important for crop growth monitoring. The identification of corn leaves is known as one of the important factors for leaf parameters estimation[7,8]. Remote sensing technique can obtain these parameters in a large imaging area in a short time[9], which is vital in agricultural application[10,11,12]. The LiDAR point clouds can be used to extract accurate leaf structure parameters non-destructively[15]. The primary work for this application is to extract leaf points[16]
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