Abstract
BackgroundThe knowledge of vine vegetative status within a vineyard plays a key role in canopy management in order to achieve a correct vine balance and reach the final desired yield/quality. Detailed information about canopy architecture and missing plants distribution provides useful support for farmers/winegrowers to optimize canopy management practices and the replanting process, respectively. In the last decade, there has been a progressive diffusion of UAV (Unmanned Aerial Vehicles) technologies for Precision Viticulture purposes, as fast and accurate methodologies for spatial variability of geometric plant parameters. The aim of this study was to implement an unsupervised and integrated procedure of biomass estimation and missing plants detection, using both the 2.5D-surface and 3D-alphashape methods.ResultsBoth methods showed good overall accuracy respect to ground truth biomass measurements with high values of R2 (0.71 and 0.80 for 2.5D and 3D, respectively). The 2.5D method led to an overestimation since it is derived by considering the vine as rectangular cuboid form. On the contrary, the 3D method provided more accurate results as a consequence of the alphashape algorithm, which is capable to detect each single shoot and holes within the canopy. Regarding the missing plants detection, the 3D approach confirmed better performance in cases of hidden conditions by shoots of adjacent plants or sparse canopy with some empty spaces along the row, where the 2.5D method based on the length of section of the row with lower thickness than the threshold used (0.10 m), tended to return false negatives and false positives, respectively.ConclusionsThis paper describes a rapid and objective tool for the farmer to promptly identify canopy management strategies and drive replanting decisions. The 3D approach provided results closer to real canopy volume and higher performance in missing plant detection. However, the dense cloud based analysis required more processing time. In a future perspective, given the continuous technological evolution in terms of computing performance, the overcoming of the current limit represented by the pre- and post-processing phases of the large image dataset should mainstream this methodology.
Highlights
Vineyards are highly heterogeneous due to structural factors mediated by topography and soil characteristics, and non-structural factors, mediated by crop practices
RGB sensors mounted on Unmanned Aerial Vehicles (UAV) are capable of providing highresolution images that can be processed to build digital surface models (DSMs), using three-dimensional (3D) reconstruction software based on stereo vision or structure from motion (SfM) algorithms Padua et al [16, 22]
In the case of a missing plant covered by vigorous shoots, the 2.5D method based on the length of section of the row with lower thickness than the threshold used (0.10 m), tended to return false negatives (Fig. 4a)
Summary
Vineyards are highly heterogeneous due to structural factors mediated by topography and soil characteristics, and non-structural factors, mediated by crop practices. RGB sensors mounted on UAVs are capable of providing highresolution images that can be processed to build digital surface models (DSMs), using three-dimensional (3D) reconstruction software based on stereo vision or structure from motion (SfM) algorithms Padua et al [16, 22] Using these methods, a large set of applications can be undertaken such as biomass monitoring [4,5,6], volume characterization Ballesteros et al [3], Matese et al [15], Pádua et al [21] and early-season crop monitoring [10], [26]. The aim of this study was to implement an unsupervised and integrated procedure of biomass estimation and missing plants detection, using both the 2.5D-surface and 3D-alphashape methods
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