Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12313/2514
Title: Open-Source Software for Crop Physiological Assessments Using High Resolution RGB Images
Authors: Kefauver, Shawn C.
Romero, Adrian Gracia
Buchaillot, Ma. Luisa
Vergara-Díaz, Omar
Fernandez-Gallego, Jose A.
El-Haddad, Georges
Akl, Alexi
Araus, José Luís
Keywords: Indexes
Agriculture
Green products
Vegetation mapping
Image color analysis
Ear
Physiology
Issue Date: 17-Feb-2021
Publisher: International Geoscience and Remote Sensing Symposium (IGARSS)
Citation: S. C. Kefauver et al., "Open-Source Software for Crop Physiological Assessments Using High Resolution RGB Images," IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 4359-4362, doi: 10.1109/IGARSS39084.2020.9324132.
Abstract: The state-of-the-art on the use of commercially available consumer color digital cameras, which capture Red, Green and Blue light covering the visible spectrum with broad spectral bands but at high spatial resolution and with accurate color calibration has produced some interesting results in recent years, bringing back the benefits of “hyperspatial” imaging for estimating various plant physiological characteristics related to both biotic and abiotic stressors. Here we will review various RGB vegetation indexes that use the spectral concept for the estimation of biomass and canopy chlorophyll, the Normalized Green Red Difference Index (NGRDI) and the Triangular Greenness Index (TGI), as well as others that are in popular use based on this same concept as more traditional style vegetation indices often used with multispectral data. We will also introduce spectral indexes based on alternate color space transforms such as Hue Saturation Intensity (HSI), CIE-Lab and CIE-Luv and their practical calculations. Practical aspects of the calculation of these RGB vegetation indexes are offered using open-source software plugins for FIJI (FIJI is Just ImageJ), including the MaizeScanner, CerealScanner, and their mobile-to-cloud ODK (Open Data Kit) versions Fusion and CerealsFusion.
URI: https://ieeexplore.ieee.org/document/9324132
ISBN: 978-172816374-1
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