# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 1 Introduction
However, to obtain a complete and accurate digitalization there should be as many perspectives as the number of the object's faces. Crops are an especially difficult object to measure due to the irregular shape of their canopies [19; 24]. Due to the capture process, static laser scanning equipment generally registers a higher point density and with higher accuracy than mobile integrated laser scanning systems [25]. However, they are less operational because, to cover a wide area and avoid occlusions, the number of scanning stations is very high, and therefore, the time to acquire the information is also high. However, the cost of acquisition is often lower than that for mobile equipment because the integrated sensors required for both types of equipment are more sophisticated for mobile systems. The postprocessing of the captured information is more laborious on static platforms because the static equipment must solve the joint of each single scan and its georeferencing. However, for the mobile and aircraft systems, the trajectory of the capture is measured, and the integration of all sensors is solved, which facilitates the matching of all the point cloud in an automatic manner. Hand-held portables also need subsequent georeferencing. The system can integrate other sensors to collect the spectral response of the scanned object. The spectral response of the object can then be integrated in the built 3D point cloud. The final point cloud jointly provides the geometry and spectral response of the scanned surfaces [26]. It can, for instance, account for the bidirectional reflectance distribution function (BDRF), which is a main issue in high-resolution remote sensing techniques in vegetation [27]. The integration of global positioning systems (GPS) has also achieved great success in geolocating and scaling geometric products [28; 29]. Greater integration has been done with an inertial measurement unit [30]. Clearly, integration of software and hardware devices expands the scanned variables and improves accuracy, although it makes processing more difficult and tedious. The use of laser sensors to digitize the 3D components of crops (particularly in viticulture) is new but promising. One of the first attempts to use laser scanning in viticulture was the studied in [31], which calculated the light interception of each vegetal organ with a laser beam mounted on a structure with an arc shape. The use of LiDAR on vineyards has increased since then. The total canopy volume can be characterized, but other agronomic parameters of interest can also be directly estimated with this technology, such as canopy height and fruit position, among many others, or indirectly estimated, such as LAI, canopy porosity, and others, through the generation of the relationships between these agronomic variables with the measured geometrical characteristics of the canopy. In addition, knowing the spatial disposition of certain plant organs could optimize some treatments of a localized nature. For example, the autonomous detection of fruits can determine the yield [32; 33; 34] or automate its harvest. Moreover, a spray application of any phytosanitary material [10; 35; 36; 37; 38], grapevine sucker detection [26], weeding, quantification of biomass storage [19; 39; 40; 41], pest prevention [42], and any other treatment that may be necessary to achieve sustainable, desired results can now be accurately applied, and even automated, with current technology. In precision viticulture, canopy characterization is directly related to the quantitative and qualitative production potential of a vineyard [34; 43; 44; 45; 46]. The canopy's structure, position, and orientation (among others) are what defines vegetal performance [46] because light interception and canopy microclimates are driving factors for energy and gas interchange and evapotranspiration. Further, in viticulture, the correct balance between vegetative growth (shoot and leaf \"production\") and reproductive development (grape production) is the key to optimizing grape production and quality [47].