# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 1 Introduction  
Excessive pesticide application leads to increased planting costs and poses threats to human health and the environment [1, 2, 3]. In recent years, to reduce pesticide usage while ensuring effective pest and disease control, precise variable-rate spraying has attracted extensive research attention [4, 5, 6, 7, 8, 9]. Canopy parameters for orchards are major factors influencing the precision of variable-rate spraying using plant-protection machinery, encompassing parameters such as canopy thickness, shape, and leaf area density. Numerous scholars have conducted extensive research on online and precise measurement methods for canopy parameters in orchards [10, 11, 12]. Ultrasonic sensors are widely used for measuring canopy parameters in orchards. However, measurement accuracy is significantly affected by angular divergence, leading to susceptibility to variations in received signal strength and typically limited measurementrange [13; 14; 15]. LiDAR sensors exhibit high measurement accuracy and stability, demonstrating excellent performance in orchard canopy parameter detection [16; 17]. Under the assumption of uniform LiDAR motion, a single-line LiDAR was used to scan a canopy made of grapevines, and multiple frames of point cloud data were fused to obtain point cloud data of the canopy within the target area [18]. The ratio of height to width of point cloud clusters and point cloud density were evaluated to effectively identify the positions of tree trunks and support rods within the canopy point cloud [19]. A self-propelled vehicle equipped with a laser scanner was used to scan pruned and symmetrically regular orchard canopies, and the geometric parameters of the canopy including height, width, surface area, and volume were also measured [20]. The functional relationship between the number of point clouds and the number of leaves was analyzed and verified by using the Matlab curve fitting toolbox cftool, and mathematical functions were employed to fit the number of point clouds to the number of leaves to calculate leaf area density [21]. A leaf area detection algorithm based on LiDAR moving speed and detection distance was proposed. In laboratory-simulated canopy detection, the root mean square error of this algorithm was 1401.17 cm\({}^{2}\), enhancing the reliability and accuracy of detection results [22]. The canopy was divided into four vertical sections and the relationship between leaf area density and the number of point clouds in different sections was explored to guide the spray. Experimental results showed that this variable-rate spraying method saved 68.30% of pesticide usage compared to traditional methods [23]. The LiDAR was used to scan the canopy structure, and the volume of the canopy section was calculated in real-time using a voxel grid method. This method achieved an accurate volume calculation in laboratory settings, with the relative error ranging from 3.30% to 9.40% [24]. A matrix analysis was conducted on the point clouds of a peanut canopy structure to obtain the canopy height characteristics. Experimental results indicated that the mean deviation from the actual results of the peanut canopy height, measured using this method, was 11% [25]. In the existing research, studies on canopy leaf density are mostly based on methods utilizing the point cloud count and the voxel grid count. Due to the resolution limitations of LiDAR, this leads to incompleteness in the scanning of leaf point clouds in canopy. The previous methods rely on leaf scanning point clouds for judgment, and can only improve measurement accuracy by continually enhancing resolution, which in turn will increase measurement and computation costs. Corresponding processing algorithms to address the incompleteness of leaf-scanning point clouds still need to be developed in order to enhance the capability to detect the leaf area density of orchard canopies. This research uses inter-frame feature-matching from LiDAR scans of the canopy to obtain multi-angle fused point clouds of orchard canopies.