Comparative leaf area index estimation using multispectral and RGB images from a UAV platform

Jenniffer Carolina Triana-Martinez; Tom De Swaef; Irene Borra-Serrano; Peter Lootens; Oscar Barrero; Jose A. Fernandez-Gallego

doi:10.1117/12.2663287

13 June 2023 Comparative leaf area index estimation using multispectral and RGB images from a UAV platform

Jenniffer Carolina Triana-Martinez, Tom De Swaef, Irene Borra-Serrano, Peter Lootens, Oscar Barrero, Jose A. Fernandez-Gallego

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Proceedings Volume 12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII; 1253906 (2023) https://doi.org/10.1117/12.2663287
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Leaf area index (LAI) is one of the most effective biophysical parameters for characterizing vegetation dynamics and crop productivity. Acquiring a time series of accurately estimated LAI in rice canopies allows to monitor and analyze growth dynamics during the crop season and contributes to a better understanding of photosynthesis, water use, biomass, and yield. Advances in technology platforms and navigation systems have enabled the acquisition of high-resolution images, offering new insights in innovative ways in an era when climate change imposes severe challenges on the agricultural sector. Field trials were conducted during two growing seasons in 2021 and 2022 in the Nataima research center of Agrosavia in El Espinal, Tolima, Colombia. The field trial consisted of three irrigation techniques applied in four Fedearroz 67 rice variety replicates. Multispectral and RGB images were taken from the UAV at 40m (1.83cm/0.49cm GSD), 60m (2.8cm/0.75cm GSD), and 80m (3.77cm/1.0cm GSD) above the crop. Images were then processed using the ViCTool, to compute vegetation indices. In addition, ground-truth LAI was indirectly determined by measuring the fresh and dry weight. Comparative results report significant differences in specific indices and trends for the two growing seasons regarding multispectral vegetation indices (NDRE, NDVI, GNDVI, GVI, SR, OSAVI, and SAVI). For the assessed RGB indices (ExG, GA, and GGA), there were no matching patterns or trends between flight height differences along cycles. These findings also reveal that although significant differences are observed, no greater improvement is seen in the determination coefficients (R2 ) for LAI estimation using linear regression at any height.

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Jenniffer Carolina Triana-Martinez, Tom De Swaef, Irene Borra-Serrano, Peter Lootens, Oscar Barrero, and Jose A. Fernandez-Gallego "Comparative leaf area index estimation using multispectral and RGB images from a UAV platform", Proc. SPIE 12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII, 1253906 (13 June 2023); https://doi.org/10.1117/12.2663287

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KEYWORDS

Vegetation

RGB color model

Linear regression

Unmanned aerial vehicles

Statistical analysis

Multispectral imaging

Crop monitoring

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