SmartHarvest - forecasting grapevine yield by means of digital technology
A precise, easy-to-handle forecasting method for grapevine productivity can assist winegrowers and winemakers in more efficiently planning grape harvest and winemaking. Such a method would reduce uncertainties regarding expected grape quantities and qualities, provide certainty in setting grape prices and planning vinification, and thus contribute to the sustainability of the wine production value chain. A method was envisioned allowing winegrowers to easily monitor berry growth and yield prediction by making use of a smartphone camera. The yield prediction capacity of the method, aiming at a maximum of 5% deviation from final grape yield, was studied during the 2018 and 2019 seasons in preliminary field trials involving physical and optical data analyses obtained from grapevine cultivars 'Pinot noir' and 'Müller-Thurgau' at the Viticulture Center of Wädenswil. The 2018 experiment showed promising preliminary results including up to 98% accuracy of overall grape yield prediction and a satisfactory correlation of 2D-imaging-based optical data with physical data, while large differences between clones were observed including a much higher deviation than 5%. The 2019 experiment yielded less accuracy of the optical data monitoring due to a change of programming approach and the limitations of the adapted 2D image data recording and processing procedure. The potential and perspectives of the method are presented and discussed.
Bertschinger, L., Cia, S., Gülan, U., Huber, J., Karakas, O., Kern, L., Mackie-Haas, K., Schneider, C., Vogelsanger, E. and Werthmüller, J. (2021). SmartHarvest - forecasting grapevine yield by means of digital technology. Acta Hortic. 1314, 139-148
viticulture, yield prediction, digitization, sustainability, lag phase