RGB picture vegetation indexes for High-Throughput Phenotyping Platforms (HTPPs)

Kefauver, Shawn C.; El-Haddad, George; Vergara-Díaz, Omar; Araus, José Luís

doi:10.1117/12.2195235

Cited by 29 publications

(45 citation statements)

References 19 publications

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“…This result is similar to a LiDAR study, which categorized wetland vegetation using LiDAR data into four types and achieved accuracies from 62.5% to 84.6% [22]. Vegetation indices, based on RGB channels, have been successfully used to describe the vegetation status [48][49][50] and also as a component to define above ground biomass [51]. Thereby, they can be approached for imagery that is recorded by cost-effective consumer cameras.…”

Section: Density and Status Assessment Of Aquatic Reed Bedssupporting

confidence: 79%

Quantification of Extent, Density, and Status of Aquatic Reed Beds Using Point Clouds Derived from UAV–RGB Imagery

et al. 2018

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Quantification of reed coverage and vegetation status is fundamental for monitoring and developing lake conservation strategies. The applicability of Unmanned Aerial Vehicles (UAV) three-dimensional data (point clouds) for status evaluation was investigated. This study focused on mapping extent, density, and vegetation status of aquatic reed beds. Point clouds were calculated with Structure from Motion (SfM) algorithms in aerial imagery recorded with Rotary Wing (RW) and Fixed Wing (FW) UAV. Extent was quantified by measuring the surface between frontline and shoreline. Density classification was based on point geometry (height and height variance) in point clouds. Spectral information per point was used for calculating a vegetation index and was used as indicator for vegetation vitality. Status was achieved by combining data on density, vitality, and frontline shape outputs. Field observations in areas of interest (AOI) and optical imagery were used for reference and validation purposes. A root mean square error (RMSE) of 1.58 m to 3.62 m for cross sections from field measurements and classification was achieved for extent map. The overall accuracy (OA) acquired for density classification was 88.6% (Kappa = 0.8). The OA for status classification of 83.3% (Kappa = 0.7) was reached by comparison with field measurements complemented by secondary Red, Green, Blue (RGB) data visual assessments. The research shows that complex transitional zones (water–vegetation–land) can be assessed and support the suitability of the applied method providing new strategies for monitoring aquatic reed bed using low-cost UAV imagery.

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Section: Density and Status Assessment Of Aquatic Reed Bedssupporting

confidence: 79%

Quantification of Extent, Density, and Status of Aquatic Reed Beds Using Point Clouds Derived from UAV–RGB Imagery

et al. 2018

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“…The use of indexes derived from RGB (red-green-blue) images taken with conventional cameras is a simple, non-destructive and cost-effective method employed in assessing crop status in field conditions, including N status and water stress [27]. RGB images have proven useful in studies evaluating the effect of abiotic stresses but have yet to be fully exploited to phenotype disease resistance [28], although RGB indexes have proven to be accurate predictors of grain yield as well as in assessing damage caused by Fusarium in wheat kernels [29] or assessing grain yield losses and resistance to yellow rust in wheat [30,31]. To the author's knowledge, although other remote sensing technologies have been used in assessing VWO, this was the first time that RGB vegetation indexes were used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

et al. 2019

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Verticillium Wilt of Olive, a disease caused by the hemibiotrophic vascular fungus Verticillium dahliae Kleb. presents one of the most important constraints to olive production in the world, with an especially notable impact in Mediterranean agriculture. This study evaluates the use of RGB vegetation indexes in assessing the effects of this disease during the biotrophic phase of host-pathogen interaction, in which symptoms of wilt are not yet evident. While no differences were detected by measuring stomatal conductance and chlorophyll fluorescence, results obtained from RGB indexes showed significant differences between control and inoculated plants for indexes Saturation, a*, b*, green Area (GA), normalized green-red difference index (NGRDI) and triangular greenness index (TGI), presenting a reduction in plant growth as well as in green and yellow color components as an effect of inoculation. These results were contrasted across two scenarios of mineral fertilization in soil and soil amended with two different olive mill waste composts, presenting a clear interaction between the host-pathogen relationship and plant nutrition and suggesting the effect of V. dahliae infection during the biotrophic phase was not related to plant water status.

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“…With the Breedpix software code, the images were processed to convert RGB values into indices based on the models of Hue-Intensity-Saturation (HIS), CIE-Lab, and CIE-Luv cylindrical-coordinate representations of colors. Additionally, Crop Senescence Index (CSI) was calculated in agreement with [15,17]. The Triangular Greenness Index (TGI) was calculated as the area of a triangle formed by the reflectance values of the Blue, Green, and Red bands [18].…”

Section: Image Processing and Statistical Analysesmentioning

confidence: 99%

“…The Normalized Difference Vegetation Index (NDVI) [11] is one of the most well-known vegetation indices derived from multispectral remote sensing, as it includes visible and near infrared radiation [12,13]. As a low-cost alternative, various RGB-based Vegetation Indices (RGB-VIs) can be calculated from commercial Reed Green Blue (RGB) cameras that have proven able to predict grain yield, quantify nutrient deficiencies, and measure disease impacts [14,15]. The RGB images can be processed using the Breedpix code that enables the extraction of RGB-VIs in relation to different properties of color, which often demonstrate a performance similar to or slightly better than that of the better-known NDVI [16].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Different Commercial and Pre-Commercial Maize Varieties under Low Nitrogen Conditions Using Affordable Phenotyping Tools

Buchaillot

Gracia‐Romero

Zaman-Allah³

et al. 2018

The 2nd International Electronic Conference on Remote Sensing

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Maize is the most commonly cultivated cereal in Africa in terms of land area and production. Low yields in this region are very often associated with issues related to low Nitrogen (N), such as low soil fertility or low fertilizer availability. Developing new maize varieties with high and reliable yields in actual field conditions using traditional crop breeding techniques can be slow and costly. Remote sensing has become an important tool in the modernization of field-based High Throughput Plant Phenotyping (HTPP), providing faster gains towards improved yield potential, adaptation to abiotic (water stress, extreme temperatures, and salinity) and biotic (susceptibility to pests and diseases) limiting conditions, and even quality traits. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and the performance of the field-based Normalized Difference Vegetation Index (NDVI) and SPAD as phenotypic traits and crop monitoring tools for assessing maize performance under managed low nitrogen conditions. Phenotyping measurements were conducted on maize plants at two different levels: on the ground and from an airborne UAV (Unmanned Aerial Vehicle) platform. For the RGB indices assessed at the ground level, the strongest correlations compared to yield were observed with Hue, GGA (Greener Green Area), and GA (Green Area) at the ground level, while GGA and CSI (Crop Senescence Index) were better correlated with grain yield at the aerial level. Regarding the field sensors, SPAD exhibited the closest correlation with grain yield, with a higher correlation when measured closer to anthesis. Additionally, we evaluated how these different HTPP data contributed to the improvement of multivariate estimations of crop yield in combination with traditional agronomic field data, such as ASI (Anthesis Silking Data), AD (Anthesis Data), and Plant Height (PH). All multivariate regression models with an R 2 higher than 0.50 included one or more of these three agronomic parameters as predictive parameters, but with RGB indices at both levels increased to R 2 over 0.60. As such, this research suggests that traditional agronomic data provide information related to grain yield in abiotic stress conditions, but that they may be potentially supplemented by RGB indices from either ground or UAV phenotyping platforms. Finally, in comparison to the same panel of maize varieties grown under optimal conditions, only 11% of the varieties that were in the highest yield-producing quartile under optimal N conditions remained in the highest quartile when

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RGB picture vegetation indexes for High-Throughput Phenotyping Platforms (HTPPs)

Cited by 29 publications

References 19 publications

Quantification of Extent, Density, and Status of Aquatic Reed Beds Using Point Clouds Derived from UAV–RGB Imagery

Quantification of Extent, Density, and Status of Aquatic Reed Beds Using Point Clouds Derived from UAV–RGB Imagery

Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

Evaluating the Performance of Different Commercial and Pre-Commercial Maize Varieties under Low Nitrogen Conditions Using Affordable Phenotyping Tools

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