**1. Introduction**

To accelerate the development of smart agriculture, agricultural vehicle navigation technology has been developed rapidly. Agricultural machinery autonomous navigation systems based on machine vision, GPS, and LiDAR sensors have emerged [1]. Machine vision is greatly affected by the operating environment and lighting conditions. The application of GPS is affected by satellite signals. A LiDAR sensor can provide a large amount of accurate distance information at a higher frequency, reliably provide the position and depth information of surrounding objects [2], and provide more comprehensive information.

There are many ways to identify fruit trees in orchards. Judging from the existing research results, LiDAR sensors, cameras, or multisensor fusion can be used to detect fruit trees. Since the overall characteristics of trees are obvious, the trunks of fruit trees can be regarded as circles which can be detected by LiDAR sensors [3]. Due to the different installation methods and types of LiDAR sensors used, the data obtained are also different. (1) A LiDAR sensor can be installed vertically to extract the contour information of fruit trees [4,5]. Although this method can obtain the information of the trunks of fruit trees, as the LiDAR sensor is installed vertically, it can only extract the information of one tree at a time. This perception method is usually used to find the specific growth information of a fruit tree, such as fruit trees contour reconstruction. (2) A ground LiDAR sensor can be used to scan the environment to obtain fruit tree information [6,7]. (3) A mobile ground LiDAR sensor has been used to identify Fuji apples [8]. (4) An airborne LiDAR sensor has been used to obtain the scan data of fruit tree trunks [9,10].

Some scholars have also obtained tree information by analyzing LiDAR sensor data found from scanning. Using the same distance between the positions of fruit trees in an orchard, the data points in the arithmetic sequence of the concave points in the LiDAR

**Citation:** Wang, Y.; Geng, C.; Zhu, G.; Shen, R.; Gu, H.; Liu, W. Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor. *Agriculture* **2022**, *12*, 914. https://doi.org/10.3390/ agriculture12070914

Academic Editor: Francesco Marinello

Received: 14 April 2022 Accepted: 18 June 2022 Published: 23 June 2022

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sensor scan data can be extracted as the trunk points to obtain data [11]. LiDAR sensors can be used to scan woodland environments to obtain woodland data [12,13]. Since the data type of LiDAR sensors can be approximated by a point set, a clustering algorithm can be used to obtain fruit tree trunk information. Two-dimensional LiDAR sensors can be used to scan orchard environments and perform data clustering to extract the arc information of trunks [14,15]. Besides obtaining fruit tree information from clustering, 2D LiDAR sensors can be used to extract the central feature point data of tree trunks using the Euclidean clustering algorithm and the important geometric theorem of three-point collinearity [16]. Three-dimensional LiDAR sensor data are more abundant than 2D LiDAR sensor data, so many people use 3D LiDAR sensors for tree detection [17,18]. Although machine vision is greatly affected by the operating environment and lighting conditions, there have been many studies on the use of cameras for fruit tree inspection in orchards [19]. Due to the complex environment of orchards, a variety of sensor fusion methods can be used for research [20–23].

In previous studies, various sensors have been used to obtain orchard environmental information for orchard intelligent equipment. Usually, the information of fruit trees is used to pave the way for the application of intelligent equipment in orchard navigation.

The main purpose of this article is to obtain the position information of fruit trees using a 2D LiDAR sensor. After obtaining the position information of fruit trees with the algorithm proposed in this paper, it can be used for positioning, fitting navigation lines, and the navigation of orchard intelligent equipment in later stages. For the complex environment of orchards, this environment perception method is studied. Firstly, a fruit tree information acquisition method based on 2D-ICP is proposed. After the iterative registration of the point cloud data of both sides of fruit trees obtained by the 2D LiDAR sensor, the point cloud data of each fruit tree in the orchard are obtained. Then, by improving the LAPO and DBSCAN algorithms, a new method based on LAPO-DBSCAN is used to obtain the position of each fruit tree and realize their detection. Finally, the accuracy of the algorithm is verified by a field test.

#### **2. Materials and Methods**

#### *2.1. Experimental Equipment*

In this research, a differential test platform with a maximum speed of 1 m/s was built, as shown in Figure 1. The LiDAR sensor scans the surrounding data in real time, and the obtained LiDAR sensor data are transmitted to the industrial computer. The industrial computer runs a self-made software system to analyze the LiDAR sensor data. The LiDAR sensor is Rashen N30103B and it adopts the horizontal installation method, which is located in the front and middle of the orchard transportation robot. The installation height is 0.65 m and the parameters are shown in Table 1.

**Figure 1.** Test platform.


**Table 1.** Two-dimensional LiDAR sensor parameters.
