Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images

Abstract

This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input.