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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 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: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agriculture; Algorithms; Autocorrelation; Cameras; Coffee; Color imagery; Computer programs; Correlation; Crop growth; Digital imaging; Image processing; Land cover; 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
• DOI: 10.1109/JSTARS.2020.3034193
• ISSN: 1939-1404

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.