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In viticulture, yield prediction plays an important role, helping winegrowers to predict the start of the next growth stage of vines and to improve vineyard management decisionmaking. To predict a vineyard’s yield, it is necessary to gather accurate local information about the vine’s phenology and morphology, such as the volume of individual grapes. Traditional collection of these data and yield prediction rely on resourceand time-intensive direct visual and manual in-field work by viticulturists. Thus, only limited sampling in the vineyards is possible, carried out by humans. Automated procedures utilizing sensor-based systems reduce the data acquisition time and enable the collection of high-resolution data from the entire vineyard. Large-scale 3D models of vineyards can be generated from these data and used to analyse, for example, the vineyard’s yield or the vegetative stage of individual vines. We propose a concept for a 3D model that uses close-range photogrammetry. In a laboratory experiment, we tested the acquisition of multi-view image datasets from grapes using close-range photogrammetry and derived physical and morphological parameters from 3D grape models. The results could contribute to the design and implementation of a large-scale in-field experiment.

Towards Predicting Vine Yield: Conceptualization of 3D Grape Models and Derivation of Reliable Physical and Morphological Parameters