# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 4 Discussion  
In studies on canopy parameter detection using ultrasonic waves [10; 14; 15], it has been found that both their accuracy and resolution need further improvement. Although some algorithms for processing echo signals have been proposed, due to the relatively weak penetration ability of ultrasonic waves and the low resolution of ultrasonic measurement data, the estimation of leaves within the canopy is inaccurate. With the continuous advancement of technology, miniaturized and LiDAR devices have been applied in canopy parameter measurements [20; 22; 24; 26]. Among the existing studies on canopy parameter measurements using LiDAR, methods based on point cloud counting and voxel grid counting are relatively commonly used. Point cloud counting can be employed to measure the canopy leaf area density. Relevant studies have further enhanced the accuracy by eliminating \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Canopy** & **Detection** & **Leaf Area** & **Predicted Leaf Area** & **Relative** \\
**Number** & **Thickness (m)** & **Distance (m)** & **Density (m\({}^{2}\)/m\({}^{3}\))** & **Density (m\({}^{2}\)/m\({}^{3}\))** & **Error (\%)** \\ \hline
1 & 0.60 & 1.20 & 3.6 & 3.8054 & 5.71 \\
2 & 0.60 & 1.40 & 3.6 & 3.7418 & 3.94 \\
3 & 0.60 & 1.60 & 3.6 & 3.8977 & 8.27 \\
4 & 0.60 & 1.80 & 3.6 & 4.0566 & 12.68 \\
5 & 0.60 & 1.50 & 3.6 & 3.9804 & 10.57 \\
6 & 0.60 & 1.50 & 2.4 & 2.0932 & \(-12.78\) \\
7 & 0.60 & 1.50 & 3.8 & 3.3833 & \(-10.97\) \\
8 & 0.60 & 1.50 & 4.5 & 3.7815 & \(-15.97\) \\
9 & 0.60 & 1.50 & 5.2 & 4.9319 & \(-5.16\) \\
10 & 0.70 & 1.50 & 3.6 & 3.9704 & 10.29 \\
11 & 0.80 & 1.50 & 3.6 & 2.9010 & \(-19.42\) \\
12 & 0.90 & 1.50 & 3.6 & 2.9621 & \(-17.72\) \\
13 & 1.00 & 1.50 & 3.6 & 2.9833 & \(-17.13\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation of canopy experiment results. Figure 5: Diagonal error plot for canopy leaf area density. non-target point clouds within the canopy. However, the resolution of LiDAR is affected by its own hardware, leading to incomplete scanning of leaf point clouds within the canopy, which introduces errors into point cloud counting-based measurements. The voxel grid method can, to a certain extent, compensate for incomplete scanning. Nevertheless, it does not consider the shape of target leaves, making it unable to achieve more precise measurements. To address this issue, this research proposes a leaf area density detection algorithm based on leaf fitting. This algorithm clusters and fits the leaf point clouds within the canopy to approximate the outer contours of the leaves. This approach can, to some extent, reduce the impact of incomplete scanning. Both indoor and outdoor experimental results in this research confirm the feasibility and accuracy of the leaf area density detection algorithm based on leaf fitting. In existing studies, unmanned aerial vehicles (UAVs) equipped with LiDAR are commonly used to measure fruit tree canopy information [11; 27; 28; 29]. The acquired point cloud data is utilized to locate the target canopy or is further processed to calculate the volume of the target canopy, providing guidance for field operations. To further enhance accuracy, researchers calculate the leaf area of the target canopy based on the point cloud data obtained from LiDAR using the voxel-based inversion method. This research measures the leaf area of orchard canopies using a ground robot equipped with LiDAR. When traveling in the orchard, the ground robot is less affected by environmental factors such as wind and airflow, enabling it to maintain a relatively stable motion trajectory and attitude. This reduces the distortion and errors in point cloud data caused by platform shaking, thereby improving the stability and accuracy of data collection. The orchard studied in this research employs a horizontal trellis cultivation system, with metal mesh structures above it. The use of UAVs for measurement would introduce point cloud noise.