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Abstract. Knowing how much pesticides to apply on a tree is still an open challenge due to inaccurate canopy density characteristics monitoring in the orchards. Accurate canopy density information can help guide the sprayer to precisely apply pesticides as needed to the target trees. Considering the challenges, the main goal of this study was to develop an unmanned ground-based canopy density measurement system aiming to support precision spraying in apple orchards. The automated measurement system comprising of a light detection and ranging (LiDAR) sensor attached to a modeled aluminum frame, an interface box for data transmission and power conversion connected to a laptop computer mounted on a utility vehicle was developed. A data processing and analysis algorithm was also developed to measure point cloud indices from the LiDAR sensor to describe the distribution of canopy density on a tree. Each of the trees was sub-divided to separately measuring point clouds of each section (six sections according to the number of the nozzles in the sprayer). Each section was bounded into an area of ~1.158m² for 2D and a volume of ~1.708m³ for a 3D-based algorithm. Canopy point density of each section was measured thereafter by counting the number of point clouds and the area of the section. The density map of each section was generated to pinpoint more accurate tree canopy density characteristics. Since accurate canopy point density information was considered, it is anticipated that the developed prototype system could be able to guide the sprayer unit for reducing the excessive pesticide usages in the orchards.