2.4.4. Simulation Data Verification

Both the LAPO and DBSCAN algorithms have shortcomings, so they need to be improved to adapt them to more scenes. Using the 2D LiDAR sensor to scan the contours of fruit trees at different heights, the point cloud data will include two kinds of point cloud data, which are the point cloud data of the main tree trunk and the point cloud data of the canopy. Usually, the more data there are, the better the algorithm will be. To verify that the LAPO-DBSCAN algorithm used in this paper is better than the LAPO and DBSCAN algorithms, simulation data similar to the trunk of fruit trees are used for verification according to the point cloud data of the trunks of fruit trees scanned by 2D LiDAR sensors, as shown in Figure 4.

**Figure 4.** Simulation data.

To prove that the method based on the LAPO-DBSCAN algorithm is able to detect more characteristic information than the method based on the LAPO algorithm, this paper uses simulated fruit tree data to test the above two methods for one hundred frames. The results obtained from the method based on the LAPO-DBSCAN algorithm and the method based on the LAPO algorithm are shown in Table 2. The simulation data used in this paper are similar to the trunk of fruit trees. Scholars [30] have used the improved DBSCAN algorithm to detect the trunk of fruit trees, and the accuracy can reach 95.5%. Compared with previous algorithms, this algorithm increases the accuracy by 3.92%. Therefore, the algorithm used in this paper will no longer be compared with the DBSCAN algorithm for detecting the trunk of fruit trees.

**Table 2.** Actual scene test results.


In Table 2, the positive detection rate of the LAPO-DBSCAN algorithm is better than that of the LAPO algorithm, and the detection result is 2.42% higher. In terms of the false detection rate, the LAPO-DBSCAN algorithm is better than the LAPO algorithm, and the difference between the detection results is 2.42%. In terms of the average processing time, the LAPO-DBSCAN algorithm consumes 82.92% less time than the LAPO algorithm. Therefore, the simulation results show that the LAPO-DBSCAN algorithm is superior to the LAPO algorithm and has a better detection effect.

#### **3. Results**

#### *3.1. Experimental Scene*

Due to the different planting mode and row spacing of each fruit tree, the data obtained by 2D LiDAR sensor scans of different fruit trees are also different, which will affect the accuracy and stability of the algorithm. The test site used in this paper was selected from the orchard of Nijiawan water field in Xiangcheng District of Suzhou, as shown in Figure 1. The distance from the ground to the main trunk of the fruit tree selected in this paper was about 0.5 m, and the area above 0.5 m was the canopy. According to the installation height of the 2D LiDAR sensor, the collected point cloud data were all the point cloud data of fruit tree crowns. As shown in Table 3, data on two rows of fruit trees used in the experiment were obtained.

**Table 3.** Fruit tree data.

