# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 1 Introduction  
Precision agriculture strategies that apply remote sensing techniques are widely used, particularly in viticulture [1]. Information obtained from satellites, airborne cameras, and ground-based sensors (among others) over the earth's surface is a trend in research and innovation activities that has been applied to precision agriculture. With these techniques, not only can we obtain the spectral response of the surface of crop from a determined point of view (aerial or ground-based), but we can also obtain the approximate crop geometry [2]. Canopies drive the main vegetal processes, such as photosynthesis, gas interchange, and evapotranspiration. These processes are directly related to sunlight interception and the microclimate generated by the plants [3]. Efforts to measure the spatial parameters in canopies have been made with simplified geometrical models, as proposed in [4], through parameters like the leaf area index (LAI), \"point quadrat\" [5; 6], leaf area density (LAD) [7], tree area index (TAI) [8; 9; 10], ground canopy cover (GCC) [11], tree row LiDAR volume (TRLV) [12], surface area density (SAD) [3], and photosynthetically active radiation (PAR) [13], among many others. Canopy characterization and monitoring help improve crop management through the estimation of water stress, the affection bypests and weeds, nutritional requirements, and final yield. This monitoring could be performed with a network of ground sensors [14; 15] and/or remote sensing techniques at any scale. When applying satellite or airborne-based remote sensing techniques, users receive the data captured by sensors as a set of images (bidimensional data) or as a set of isolated measurements. However, less attention has been paid to other types of information that can contribute significantly to the geometry characterization of the canopy, such as 3D point clouds. It is possible to obtain an accurate 3D model of a crop from aerial imagery using photogrammetry techniques [2; 16; 17; 18], but the point of view of these images does not build a true 3D model. Instead, it builds what is called a 2.5D model. These flights obtain images from a nadir perspective, so all objects are projected on a horizontal plane. The lower part of the canopy structure of the plant is hidden from the sensor, and therefore, it is ignored in the data acquisition process. To solve this limitation, laser scanning is becoming a promising technology in precision agriculture. These systems can be mounted on static tripods [19], aircraft [20], land vehicles [8], or be used as hand-held systems [21; 22], so scanning can be done from several perspectives. Further, laser scanning systems can be mounted on drones, which facilitates data acquisition in the process of biomass mapping [23]. However, the autonomy of these systems can limit the applicability of these types of systems. Point clouds taken by active sensors, such as laser scanners, are generated via the return of light pulses that are emitted and received by the sensor. A light pulse is emitted from a known point in space with a specific direction. This pulse travels in a straight line through the air until it intercepts the object's surface where the pulse is reflected. The distance between the sensor and the scanned surface is measured by receiving the return with three possible methods, depending on the construction of the equipment: (1) the time of flight (TOF); (2) the phase shift; or (3) optic triangulation. The location of the scanned point can be estimated by comparing it with the base. These sensors are commonly accompanied by a rotating mirror converting a single scan direction to a full plane of scan directions (perpendicular to the rotated axis). The platform where the equipment is mounted is used to determine if the scanning is performed from a fixed point (static terrestrial laser scanner) or a trajectory (mobile mapping, hand-held, or aircraft). Mobile systems can measure the objects from several perspectives.