Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images

Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evoluti...

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Main Authors: Luana Mendes dos Santos, Gabriel Araujo e Silva Ferraz, Brenon Diennevan de Souza Barbosa, Adriano Valentim Diotto, Marco Thulio Andrade, Leonardo Conti, Giuseppe Rossi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9240961/
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spelling doaj-b8f684aefbd24812a2e26e4786da0b8b2021-06-03T23:08:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01136401640910.1109/JSTARS.2020.30341939240961Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB ImagesLuana Mendes dos Santos0https://orcid.org/0000-0001-8406-2820Gabriel Araujo e Silva Ferraz1https://orcid.org/0000-0001-6403-2210Brenon Diennevan de Souza Barbosa2https://orcid.org/0000-0001-6791-2504Adriano Valentim Diotto3https://orcid.org/0000-0002-4019-2444Marco Thulio Andrade4https://orcid.org/0000-0002-5480-8780Leonardo Conti5https://orcid.org/0000-0002-4181-5893Giuseppe Rossi6https://orcid.org/0000-0003-0211-9294Department of Agricultural Engineering, Federal University of Lavras, University Campus, Lavras, BrazilDepartment of Agricultural Engineering, Federal University of Lavras, University Campus, Lavras, BrazilDepartment of Agricultural Engineering, Federal University of Lavras, University Campus, Lavras, BrazilDepartment of Water Resources and sanitation, Federal University of Lavras, Lavras, BrazilDepartment of Agricultural Engineering, Federal University of Lavras, University Campus, Lavras, BrazilDepartment of Agricultural, Food, Environment and Forestry (DAGRI), University of Florence, Florence, ItalyDepartment of Agricultural, Food, Environment and Forestry (DAGRI), University of Florence, Florence, ItalyLeaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3-D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to an unmanned aerial vehicle (UAV). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R<sup>2</sup> and RMSE values of 89% and 3.41, respectively, while those for LAI had R<sup>2</sup> and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February, and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and center-west regions presenting LAI values &gt; 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.https://ieeexplore.ieee.org/document/9240961/Coffeeleaf area index (LAI)point cloudstructure from motion (SfM)unmanned aerial vehicle (UAV)
collection DOAJ
language English
format Article
sources DOAJ
author Luana Mendes dos Santos
Gabriel Araujo e Silva Ferraz
Brenon Diennevan de Souza Barbosa
Adriano Valentim Diotto
Marco Thulio Andrade
Leonardo Conti
Giuseppe Rossi
spellingShingle Luana Mendes dos Santos
Gabriel Araujo e Silva Ferraz
Brenon Diennevan de Souza Barbosa
Adriano Valentim Diotto
Marco Thulio Andrade
Leonardo Conti
Giuseppe Rossi
Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Coffee
leaf area index (LAI)
point cloud
structure from motion (SfM)
unmanned aerial vehicle (UAV)
author_facet Luana Mendes dos Santos
Gabriel Araujo e Silva Ferraz
Brenon Diennevan de Souza Barbosa
Adriano Valentim Diotto
Marco Thulio Andrade
Leonardo Conti
Giuseppe Rossi
author_sort Luana Mendes dos Santos
title Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images
title_short Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images
title_full Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images
title_fullStr Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images
title_full_unstemmed Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images
title_sort determining the leaf area index and percentage of area covered by coffee crops using uav rgb images
publisher IEEE
series IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
issn 2151-1535
publishDate 2020-01-01
description Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3-D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to an unmanned aerial vehicle (UAV). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R<sup>2</sup> and RMSE values of 89% and 3.41, respectively, while those for LAI had R<sup>2</sup> and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February, and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and center-west regions presenting LAI values &gt; 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.
topic Coffee
leaf area index (LAI)
point cloud
structure from motion (SfM)
unmanned aerial vehicle (UAV)
url https://ieeexplore.ieee.org/document/9240961/
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