Biophysical parameters of coffee crop estimated by UAV RGB images

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Biophysical parameters of coffee crop estimated by UAV RGB images Luana Mendes dos Santos1   · Gabriel Araújo e Silva Ferraz1   · Brenon Diennevan de Souza Barbosa1   · Adriano Valentim Diotto2   · Diogo Tubertini Maciel1 · Letícia Aparecida Gonçalves Xavier1 

© Springer Science+Business Media, LLC, part of Springer Nature 2020

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. Keywords  Remote sensing · Tree height · Unmanned aircraft system · Structure from motion · Aerial survey

Introduction The global coffee production for the 2019/2020 harvest is estimated at 10.2 million tonnes (169.3 million bags). Brazil has a significant share of this production, occupying the top position with a 34.2% share of the coffee production worldwide (USDA 2020). It is the * Luana Mendes dos Santos [email protected] 1

Agricultural Engineering Department, Federal University of Lavras, P.O BOX 3037, Lavras, MG CEP 37200‑000, Brazil

2

Department of Water Resources and Sanitation, Federal University of Lavras, P.O BOX 3037, Lavras, MG CEP 37200‑000, Brazil



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Precision Agriculture

largest producer of Arabica coffee, accounting for 41.7% of the global production, and the second largest producer of Robusta coffee, accounting for 24.6% of the global production of this cultivar. Advances in digital agriculture combined with computational tools, UAV and optical sensors (multispectral cameras) have enabled the collection of data that enable the reliable extraction of vegetation indices and biophysical parameters using the SfM algorithm. The SfM is based on the principles of traditional stereoscopic photogrammetry, in which multiple photos are overlapped to obtain geometric features generating 2D and 3D point clouds obtained by conventional cameras (Bendig et  a

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