Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications

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ID: 86039
2020
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Abstract
Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications.
Reference Key
mesascarrascosa2020classificationremote Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mesas-Carrascosa, Francisco-Javier;Castro, Ana I. de;Torres-Sánchez, Jorge;Triviño-Tarradas, Paula;Jiménez-Brenes, Francisco M.;García-Ferrer, Alfonso;López-Granados, Francisca;
Journal remote sensing
Year 2020
DOI DOI not found
URL
https://www.mdpi.com/2072-4292/12/2/317
Keywords
Engineering (General). Civil engineering (General) Technology Literature (General) Science environmental effects of industries and plants renewable energy sources environmental sciences economics as a science language and literature applied optics. photonics sociology (general) communication. mass media special aspects of education advertising

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