Abstract
In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R2 was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R2 = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight.
Highlights
The 107 million hectolitre international demand for wine in 2018 translated into EUR 9 × 109 in export revenues for France, EUR 6 × 109 for Italy, and EUR 3 × 109 for Spain, Spain exported a higher volume than either of its neighbours [1]
In light of the foregoing discussion, the main objective of this study was estimate pruning weight in vineyards from a 3D point cloud generated with conventional images captured by a standard drone and processed by structure from motion photogrammetry
All the images captured by the drones were used to build the point cloud
Summary
The 107 million hectolitre international demand for wine in 2018 translated into EUR 9 × 109 in export revenues for France, EUR 6 × 109 for Italy, and EUR 3 × 109 for Spain, Spain exported a higher volume than either of its neighbours [1] Those figures provide eloquent proof of the need to efficiently produce quality wine to compete effectively on an ever-more demanding market and ensure the profitability of wine production. Routine data collection of that nature enables farmers to adopt the sort of decisions early in the season that favour the production of higher quality wine at a lower cost Variables such as plant height, canopy volume or leaf area may afford growers with information on canopy structure, estimated production, and vine condition, parameters that can be used to predict harvest quantity and quality [2,3,4,5]. The vast resources required for such endeavours have induced researchers to analyse remote sensing tools to lower the economic cost of and time devoted to standard sampling for more efficient information gathering on vine condition [8]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
