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metadata.artigo.dc.title: Biophysical parameters of coffee crop estimated by UAV RGB images
metadata.artigo.dc.creator: Santos, Luana Mendes dos
Ferraz, Gabriel Araújo e Silva
Barbosa, Brenon Diennevan de Souza
Diotto, Adriano Valentim
Maciel, Diogo Tubertini
metadata.artigo.dc.publisher: Springer 2020
metadata.artigo.dc.identifier.citation: SANTOS, L. M. de et al. Biophysical parameters of coffee crop estimated by UAV RGB images. Precision Agriculture, [S.l.], 2020.
metadata.artigo.dc.description.abstract: The advance of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has enabled the collection of data for reliably extracting vegetation indices and biophysical parameters derived from the Structure from Motion (SfM) algorithm. This work aimed to evaluate the accuracy of the photogrammetry technique using an SfM point cloud for the estimation of the height (h) and crown diameter (d) of coffee trees from aerial images obtained by UAV with an RGB (Red, Green, Blue) camera and compared the results with data measured in situ for 12 months. The experiment was carried out in a coffee plantation, Lavras, Minas Gerais, Brazil. A rotary-wing UAV was used in autonomous flight mode and coupled to a conventional camera, flying at a height of 30 m with an image overlap of 80% and a speed of 3 m/s. The images were processed using PhotoScan software, and the analyses were performed in Qgis. A correlation of 87% was obtained between the h values in the field and h values obtained by the UAV, and there was a 95% correlation between the d values obtained in the field and the values obtained by the UAV. It was possible to obtain significant estimates of the attributes, such as the h and d of coffee trees, using UAV–SfM images acquired with an RGB digital camera.
metadata.artigo.dc.language: en_US
Appears in Collections:DEG - Artigos publicados em periódicos

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