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

Brenon Diennevan de Souza Barbosa¹  Marco Thulio Andrade¹  Luana Mendes dos Santos¹  Gabriel Araujo e Silva Ferraz¹  Leonardo Conti²  Giuseppe Rossi²  Adriano Valentim Diotto³ 

[1] Department of Agricultural Engineering, Federal University of Lavras, University Campus, Lavras, Brazil;Department of Agricultural, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy;Department of Water Resources and sanitation, Federal University of Lavras, Lavras, Brazil;

关键词: Coffee;    leaf area index (LAI);    point cloud;    structure from motion (SfM);    unmanned aerial vehicle (UAV);   

DOI  :  10.1109/JSTARS.2020.3034193

来源: DOAJ

【 摘 要 】

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 R² and RMSE values of 89% and 3.41, respectively, while those for LAI had R² 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.

【 授权许可】

Unknown