## 1 Introduction  
Precision agriculture is focused on the use of various technologies to monitor the spatial and temporal variability related to agricultural management, yield maximization, and economic and environmental benefits [1]. In precision viticulture, the technological advances used in precision agriculture are used to improve yield forecasting, harvesting management, water management, and the improvement of grape quality, which then in turn influences the quality of the bottled product, namely, wine. In recent decades, the development of uncrewed aerial systems (UAS) technology and satellite technology for remote sensing has experienced an increase in spatial resolution, temporal availability, and the capability to describe the physiological, geometrical, and morphological parameters of plants [2]. Vegetation indices, calculated based on multispectral sensor data from UAS or multispectral satellite data, are used to monitor plant status, stress levels, and vineyard variability. The index values are correlated to chlorophyll content within leaves, nitrogen concentration, and plant water status for vine variability and vigor monitoring [3]. Besides vineyard variability monitoring, the estimation of vine row area and leaf area, disease detection, and vineyard vigor mapping, yield estimation is another important topic [1]. Yield estimation is used for wine cell management and to calculate the grape purchases from external sources if the forecast of the yield is below the needs of the winery, or to intensify the selling of grapes if the yield is above the expectations. Therefore, it is of great importance to monitor the yield of a vineyard to work profitably and accurately. However, optimizing yield estimation requires current and georeferenced vineyard information regarding the parameters, including crop height, canopy architecture, and fruit weight [4]. From all collectable datasets within a vineyard, data related to yield estimation stand out for their economic relevance and for being of great importance for optimizing plant growth and fruit quality [5]. The optimization of vineyard management techniques often requires efficient and automated or semi-automated methods in order to identify vine-specific morphological and geometric parameters like canopy architecture, vine heights, vine row geometry, and vine location within rows. UAS-based imagery offers the capability of modeling the plant-specific morphological parameters using photogrammetric processing [6]. Photogrammetry can provide a non-contact 3D reconstruction of objects that can be beneficial if the objects are spread over a large area or are difficult to reach. Structure from motion (SfM) processing combines photogrammetric data acquisition techniques with computer vision in order to reconstruct 3D surfaces [7]. The algorithm uses a technique of comparing recurring points in image sets to reconstruct the UAS camera positions. The process results in a sparse point cloud, containing the triangulated locations of points that were matched across the images [8]. The generated output requires imagery from different angles and viewpoints to reliably reconstruct the geometry of the area under study. The image acquisition and the photogrammetric processing are followed by the output creation. The outputs of the digital image processing are a dense 3D point cloud, a textured 3D model, a digital surface model (DSM), and an orthomosaic [9]. UAS are among the most important sensor platforms in modern precision agriculture. They show great potential in precision viticulture thanks to their capacity to produce very high spatial, spectral, and temporal resolution with a ground sampling distance (GSD) up to 1 cm; the possibility to apply multispectral sensors; and the option of a high repeatability rate. Sensors onboard UAS can collect a variety of different data sets that are used to calculate vegetation indices for monitoring the vitality and the geometric and morphological characteristics of the objects under study. [PERSON] et al.