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

Abstract

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

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

“…Vegetation indices can be derived from RGB color space indices, which represent international standards for color perception by the human eye and were adopted by the Commission Internationale de l'Eclairage (CIE) in 1976 (Yam and Papadakis, 2004;Trussell et al, 2005;Casadesús et al, 2007;Liu et al, 2011;Kipp et al, 2014;Zhou et al, 2015). Several studies show that RGB color space indices, such as hue, a * , u * , and other derived indices, such as green area (GA) and the normalized difference CIELab index (NDlab), outperformed spectral indices such as NGRDI, NDVI, and gNDVI, in predicting yield of wheat and maize more accurately and had higher broad sense heritability for drought tolerance in forage grasses (Kefauver et al, 2015;Vergara-Díaz et al, 2015Zhou et al, 2015;Gracia-Romero et al, 2017Buchaillot et al, 2018Buchaillot et al, , 2019Fernandez-Gallego et al, 2019;De Swaef et al, 2021).…”

Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

“…The greener area (GGA) excludes yellow pixels that correlate with senescent leaves [31]. These indices generally correlate to green biomass and are used to calculate the Crop Senescence Index (CSI), which is the scaled ratio between yellow and green vegetation pixels calculated using the following formula: CSI = (GA − GGA)/GA * 100 [21]. This index is correlated with leaf senescence.…”

“…Several indices have been developed that successfully utilize data from both multispectral and RBG imagery as a lowcost alternative to predict biomass [19,20]. The RBG image indices derived from models of Hue-Intensity-Saturation (HIS), International Commission on Illumination L*a*b* (CIELab), and L*u*v* (CIELuv) cylindrical coordinate representations of colors [18,[21][22][23] have been useful in predicting crop yield and, in some cases, have been more accurate than multispectral methods [22,24,25]. RGB indices have been correlated with the sugarcane biomass in other crops [19,20]; therefore, if similar results are obtained with sugarcane seedlings, then this procedure may be used to identify sugarcane seedlings with high biomass.…”