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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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 > 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 > 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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