Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Abstract

Leaf area index (LAI), green canopy cover (GCC), and canopy volume (V) are associated with grape vigor, quality, and yield. Thus, analyzing these parameters throughout the growing season may help optimize site-specific management of grape vineyards. Because direct measurements of LAI are destructive, tedious, and not repeatable on the same vine, developing and validating nondestructive methods to estimate LAI are essential. Canopy pattern is characterized by GCC and V, which can be measured using aerial observation. The purpose of this study was to characterize growth parameters, such as LAI, GCC, and V, of irrigated and rainfed Vitis vinifera L. under semiarid conditions on two different vineyards using aerial images from an unmanned aerial vehicle. The relationships between GCC versus LAI and V versus LAI were calculated and validated. Relationships between LAI and the other parameters depend on canopy management, training system, and pruning practices. Relationships between LAI and growing degree days (GDD) and V and GDD were also obtained to determine the canopy structure pattern during the growing season. Exponential polynomial and second-order polynomial models showed the best fit for describing the relationships between GCC and GDD and between V and GDD, respectively, for Airén variety.