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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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.6401-6409
Main Authors: Mendes dos Santos, Luana, Ferraz, Gabriel Araujo e Silva, Barbosa, Brenon Diennevan de Souza, Diotto, Adriano Valentim, Andrade, Marco Thulio, Conti, Leonardo, Rossi, Giuseppe
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Autocorrelation
Cameras
Coffee
Color imagery
Computer programs
Correlation
Crop growth
Crop yield
Digital imaging
Image processing
Land cover
Land use
Leaf area
Leaf area index
leaf area index (LAI)
Leaves
point cloud
Prediction models
Regression analysis
Remote sensing
Software
Spatial analysis
Stereophotogrammetry
structure from motion (SfM)
Three dimensional models
Three-dimensional displays
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
Yield forecasting
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Summary: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 2 and RMSE values of 89% and 3.41, respectively, while those for LAI had R 2 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.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.3034193