Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture

An effective management of precision viticulture processes relies on robust crop monitoring procedures and, in the near future, to autonomous machine for automatic site-specific crop managing. In this context, the exact detection of vineyards from 3D point-cloud maps, generated from unmanned aerial vehicles (UAV) multispectral imagery, will play a crucial role, e.g. both for achieve enhanced remotely sensed data and to manage path and operation of unmanned vehicles. In this paper, an innovative unsupervised algorithm for vineyard detection and vine-rows features evaluation, based on 3D point-cloud maps processing, is presented. The main results are the automatic detection of the vineyards and the local evaluation of vine rows orientation and of inter-rows spacing. The overall point-cloud processing algorithm can be divided into three mains steps: (1) precise local terrain surface and height evaluation of each point of the cloud, (2) point-cloud scouting and scoring procedure on the basis of a new vineyard likelihood measure, and, finally, (3) detection of vineyard areas and local features evaluation. The algorithm was found to be efficient and robust: reliable results were obtained even in the presence of dense inter-row grassing, many missing plants and steep terrain slopes. Performances of the algorithm were evaluated on vineyard maps at different phenological phase and growth stages. The effectiveness of the developed algorithm does not rely on the presence of rectilinear vine rows, being also able to detect vineyards with curvilinear vine row layouts