## 1 Introduction
[21], LiDAR sensors stand out for field of view (FOV), angular resolution, response time and number of scan points. For example, the FOV represents the area that can be observed by the scanner and is limited to the horizontal plane for 2D scanners and both horizontal and vertical planes for 3D scanners. LiDAR systems can be classified into four different scanning mechanisms: optomechanical, electromechanical, MEMS and solid-state scanning [21; 22]. Optomechanical scanning uses optical components in aiding beam deflection and is similar to MEMS scanning, but the latter is smaller and lighter; electromechanical scanning uses electric motors to obtain 3D scans due to different beam orientations, and solid-state scanning is used to detect objects for navigation purposes. There are different kinds of LiDARs, for example, LS Lidar MS03 [23] is a 3D LiDAR that scans at high speed at a horizontal angle of 120 deg, an accuracy of \(\pm\)2 cm and acquisition frequency variable from 75 kHz to 750 kHz, based on a detection range from 2000 m to 200 m. Sick TiM1 xx [24] is a 2D LiDAR that scans at a horizontal angle of 200 deg for indoor use and an acquisition frequency about 14.5 Hz. The first one is much more expensive than the second one, respectively, around EUR 25-30,000 and EUR 900-1000. The distribution of agrochemicals is deeply linked to spray drift, defined as a part of agrochemicals carried out of the target during the spraying activity [25]. According to the literature, a spray losses range from 1% up to 20-30% [26; 27], but in some cases, it can reach 90%, depending on the compounds used [28]. Spray drift affects human health and environment, including water, soil and air. Therefore, it is important to reduce agrochemicals used in agriculture and, at the same time, improve the distribution on the target [29; 30]. Thanks to new technologies, it is possible to manage the distribution of agrochemicals and to use the correct amount on the correct target [30] following the principles of precision agriculture. There are two possibilities: variable rate spraying, managing the distribution of agrochemicals according to specific vegetation features, or on/off control, as explained below. The spray cloud can be analyzed by a 3D LiDAR;the data can be used both to modulate the distribution of agrochemicals and develop a simple prediction model [26]. Furthermore, LiDAR gave satisfactory results applied to on/off control based on the presence/absence of vegetation [31]. New technologies demonstrate their positive performance, for example, the reduction of pesticides ranges from 53% [32] up to 70% [33], depending on the type of innovation introduced. The reduction of agrochemicals distributed, combined with better distribution on the target, allows to reduce spray drift phenomena. Spray drifts can be detected with an artificial target, called water-sensitive papers (WSP), subsequently analyzed with different techniques. There are three cases found about the use of WSP to detect spray distribution [34; 35; 36]. In particular, the study conducted by [PERSON] et al. [36] represents an important starting point for the work discussed below. They used monofilament nylon screens to mount WSP (26 \(\times\) 76 mm, by Syngenta Crop Protection AG, Basel, Switzerland) in different target locations inside the orchard; one of the procedures reported for data analysis is based on coverage WSP samples provided by the chemical company (hereafter referred to as the \"comparing protocol\"). The analysis of water-sensitive papers is primarily direct to observe the coverage area, expressed as a percentage of the covered area; there are different techniques to elaborate WSP: digital post-processing and data extrapolation via software, a smart camera equipped with internal software or portable applications for a smartphone. The former solution is the most hardworking because of the complexity of each step: [PERSON] et al.