Knowledge of tree size is of great importance for the precision management of a hazelnut orchard. In fact, it has been shown that site-specific crop management allows for the best possible management and efficiency of the use of inputs. Generally, measurements of tree parameters are carried out using manual techniques that are time-consuming, labor-intensive and not very precise. The aim of this study was to propose, evaluate and validate a simple and innovative procedure using images acquired by an unmanned aerial vehicle (UAV) for canopy characterization in an intensive hazelnut orchard. The parameters considered were the radius (R_c), the height of the canopy (h_c), the height of the tree (h_tree) and of the trunk (h_trunk). Two different methods were used for the assessment of the canopy volume using the UAV images. The performance of the method was evaluated by comparing manual and UAV data using the Pearson correlation coefficient and root mean square error (RMSE). High correlation values were obtained for R_c, h_c and h_tree while a very low correlation was obtained for h_trunk. The method proposed for the volume calculation was promising.

The suitability of a smartphone camera for the structure from motion (SfM) reconstruction for monitoring variations in soil surface characteristics and soil loss originated by a low intensity erosive event was evaluated. Terrestrial laser scanning (TLS) was used to validate the SfM model. Two surveys of the soil surface, one before and one after the rainfall event, were carried out for SfM and TLS. The point clouds obtained by the SfM were compared to the TLS point clouds (used as reference). From the point clouds, digital elevation models (DEMs) (0.01 m × 0.01 m) were obtained. The differences of the DEMs (DoDs) obtained from the two surveys for SfM and TLS were compared. To assess the uncertainty of the DEMs, from the DoDs the minimum level of detection was derived. The soil loss was evaluated from DoDs (for SfM and TLS, respectively) considering negative values as erosion and positive values as deposition. The SfM appears appropriate and sensitive for detecting small soil surface variations induced by low erosive events. The SfM estimated correctly the measured soil loss, while TLS underestimated 26%. Further studies could be carried out to consolidate these first results.