Vineyard detection from unmanned aerial systems images

Abstract

In viticulture, the adoption of precision agriculture techniques is nowadays increasingly essential to reach required high product quality standards. New reliable tools for mapping crop variability indexes in a vineyard or a single parcel are necessary to deploy site-specific management practices. In this paper, a new method to automatically detect vine rows in gray-scale aerial images is presented. The developed image processing algorithm is constituted by three main steps based on dynamic segmentation, Hough Space Clustering and Total Least Squares techniques. The procedure’s reliability has also been proven in the presence of disturbance elements, like dense inter-row grassing, bushes and trees shadows, by properly detecting vine rows in the vineyard images. Moreover, its adaptive features allow it to obtain optimal results in the presence of uneven image illumination due, for example, to the presence of clouds or steep terrain slopes. The extracted row and inter-row information, besides being the basis for vineyard characterization maps computation, like vine plants vigor maps, could also be used as a reference for other precision viticulture tasks such as, for example, path planning of unmanned ground vehicles.