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Biophysical parameters of coffee crop estimated by UAV RGB images

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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.

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Acknowledgements

The authors would like to thank the National Council for Scientific and Technological Development (CNPq) for the financing of equipment and the Coordination for the Improvement of Higher Education Personnel (Capes) for the granting of scholarships.

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Authors and Affiliations

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

    Luana Mendes dos Santos, Gabriel Araújo e Silva Ferraz, Brenon Diennevan de Souza Barbosa, Diogo Tubertini Maciel & Letícia Aparecida Gonçalves Xavier

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

    Adriano Valentim Diotto

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  1. Luana Mendes dos Santos
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  2. Gabriel Araújo e Silva Ferraz
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  3. Brenon Diennevan de Souza Barbosa
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  5. Diogo Tubertini Maciel
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  6. Letícia Aparecida Gonçalves Xavier
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Correspondence to Luana Mendes dos Santos.

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dos Santos, L.M., Ferraz, G.A.e.S., Barbosa, B.D.S. et al. Biophysical parameters of coffee crop estimated by UAV RGB images. Precision Agric 21, 1227–1241 (2020). https://doi.org/10.1007/s11119-020-09716-4

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