# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 3 Results
### Experiment Verification Results  
The results of the laboratory-simulated canopy validation experiment are shown in Table 6, and the absolute value of average relative error is 11.58%. When the canopy thickness (0.60 m) and actual leaf area density (3.6 m\({}^{2}\)/m\({}^{3}\)) remain constant, as the detection distance changes, the system's measurement results are consistently higher than the actual values. The difference ranges from 0.21 m\({}^{2}\)/m\({}^{3}\) to 0.46 m\({}^{2}\)/m\({}^{3}\). As the detection distance increases, the detection angle gradually decreases. During the canopy thickness measurement, the number of detected point cloud points from the rear decreases, leading to an underestimation of the measured canopy thickness. Consequently, this results in an overestimation of the leaf area density. The overall average error in the leaf area density measurement is 8.23%. When the canopy thickness (0.6 m) and detection distance (1.5 m) remain constant, the overall measurement discrepancy increases as the actual leaf area density rises. With the gradual increase in leaf area density, the number of leaves within the canopy increases, leading to more overlapping among leaves. When the actual leaf area density reaches 5.2 m\({}^{2}\)/m\({}^{3}\), the measurement discrepancy decreases. Due to the occlusion effect, leaves in the rear of the canopy cause the detected point cloud data to become sparse and reduced in quantity. The non-uniform distribution of the point cloud tends to result in an underestimation of the measured canopy thickness. Consequently, this leads to an overestimation of the leaf area density measurement, which partially counteracts the overlapping error, ultimately reducing the overall discrepancy. When the detection distance (1.5 m) and actual leaf area density (3.6 m\({}^{2}\)/m\({}^{3}\)) remain constant, the difference between the measured and true values ranges from 0.37 m\({}^{2}\)/m\({}^{3}\) to 0.70 m\({}^{2}\)/m\({}^{3}\) as the canopy thickness varies. When the canopy thickness exceeds 0.70 m, the discrepancy becomes significantly larger. As the canopy thickness increases, the point cloud data from the deeper layers becomes sparser during scanning, leading to an underestimation of the measured leaf area compared to the actual value. When the canopy thickness is small, the relative error is also small, indicating high accuracy. However, as the canopy thickness increases, the relative error gradually enlarges, suggesting that the detection accuracy of this algorithm will decrease when processing thicker canopies. Nevertheless, this algorithm still has certain advantages in overall performance and shows potential in detecting leaf area density. The experimental results for the orchard are shown in Figure 5 below. The absolute average relative error of observed mean leaf area density and predicted mean leaf area density is 16.75% and maximum and minimum errors are 34.58% and 0.77%. When sampling occurs at the junction of the canopies of two trees, the leaves are concentrated on both sides of the sampling area, while the point cloud in the middle is relatively sparse, which can lead to missed and false detections of the canopy. When the leaf area density at the sampling point is below 4 m\({}^{2}\)/m\({}^{3}\), the average relative error is 10.14%. The leaf area density detection based on the fitted leaf method demonstrates good applicability and accuracy. Within a certain range of leaf area density, the leaf area density detection method based on the fitted leaf approach demonstrates good applicability and accuracy.