Edited by: Yanbo Huang, United States Department of Agriculture, United States
Reviewed by: Jingcheng Zhang, Hangzhou Dianzi University, China; Maria Balota, Virginia Tech, United States
*Correspondence: José L. Araus
This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Low soil fertility is one of the factors most limiting agricultural production, with phosphorus deficiency being among the main factors, particularly in developing countries. To deal with such environmental constraints, remote sensing measurements can be used to rapidly assess crop performance and to phenotype a large number of plots in a rapid and cost-effective way. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and multispectral (visible and infrared) data as phenotypic traits and crop monitoring tools for early assessment of maize performance under phosphorus fertilization. Thus, a set of 26 maize hybrids grown under field conditions in Zimbabwe was assayed under contrasting phosphorus fertilization conditions. Remote sensing measurements were conducted in seedlings at two different levels: at the ground and from an aerial platform. Within a particular phosphorus level, some of the RGB indices strongly correlated with grain yield. In general, RGB indices assessed at both ground and aerial levels correlated in a comparable way with grain yield except for indices a* and u*, which correlated better when assessed at the aerial level than at ground level and Greener Area (GGA) which had the opposite correlation. The Normalized Difference Vegetation Index (NDVI) evaluated at ground level with an active sensor also correlated better with grain yield than the NDVI derived from the multispectral camera mounted in the aerial platform. Other multispectral indices like the Soil Adjusted Vegetation Index (SAVI) performed very similarly to NDVI assessed at the aerial level but overall, they correlated in a weaker manner with grain yield than the best RGB indices. This study clearly illustrates the advantage of RGB-derived indices over the more costly and time-consuming multispectral indices. Moreover, the indices best correlated with GY were in general those best correlated with leaf phosphorous content. However, these correlations were clearly weaker than against grain yield and only under low phosphorous conditions. This work reinforces the effectiveness of canopy remote sensing for plant phenotyping and crop management of maize under different phosphorus nutrient conditions and suggests that the RGB indices are the best option.
Sub-Saharan Africa (SSA) has one of the world's fastest growing populations but the growth rate of food production has not kept pace with this, leading to a food deficit (Mclntyre et al.,
Remote sensing has become an important methodology for the application of agricultural monitoring and to improve precision and throughput in phenotyping. There is a growing body of literature demonstrating the usefulness of remote sensing for a wide range of applications in agriculture: growth monitoring, yield prediction, stress detection, nutrient deficiency diagnosis, and control of plant diseases (Fiorani and Schurr,
The traditional procedure has involved the use of multispectral sensors and the development of numerous vegetation indices associated with vegetation parameters such as above-ground biomass, water and nutrient deficiency, and crop yield (Petropoulos and Kalaitzidi,
The use of information derived from conventional digital RGB (of red, green, blue) images may represent a low-cost alternative to the use of multispectral or hyperspectral information for formulating vegetation indices. The images can be processed to convert RGB values into indices based on the models of Hue-Intensity-Saturation (HIS), CIELab, and CIELuv cylindrical-coordinate representations of colors. The RGB indices implementation has been extensive and successful for providing a wide-range of phenomic data about genotypic performance under different growing conditions (Casadesús et al.,
The environmental variability throughout the day, like changes in radiation, temperature or the occurrence of clouds, affects the phenotypic observations inconsistently and may limit the accuracy of the time-consuming proximal measurements at ground level (e.g., the relative chlorophyll content). The incorporation of these methodologies into aerial based platforms enables the simultaneous characterization of a larger number of plots (i.e., spectral reflectance at solar noon), which may help to minimize the effect of changing environmental conditions (Araus and Cairns,
The vegetation indices, formulated from the visible and infrared spectrum of the light reflected by plants or derived from RGB conventional digital images are the most usual remote sensing method to assess plant nutrient status (Vergara-Díaz et al.,
Because maize is among the major crops globally, and the main staple for direct human consumption in SSA (Cairns et al.,
Field trials were carried out at the Southern Africa regional station of CIMMYT (International Maize and Wheat Improvement Center) located in Harare (−17.800, 31.050, 1498 masl), Zimbabwe. The soil in the station is characterized by a pH slightly lower than 6, nitrogen as nitrate (
A set of 25 maize hybrids developed at CIMMYT plus a local check (CZH131001, CZH0524, CZH141042, CZH0631, CZH131002, CZH0513, CZH131007, CZH03042, CH12716, CZH03004, CZH15020, SC513, CZH132210, CZH142125, CZH132218, CZH142153, CZH142159, SC719, CZH142186, CZH142212, CZH142074, CZH142003, CZH142206, CZH142195, and CZH142210) were sown during the wet season on December 2015. These maize hybrids reflect a large variability in plant performance to different phosphorous conditions. The experimental design consisted of two separated phosphorous treatments with 26 plots each corresponding to each maize genotype studied (52 plots in total).
Seeds were planted on December 21st 2015, in three rows per plot; rows were 4 m long and 75 cm apart (9 m2/plot), with 17 plants per row and 25 cm between plants in each a row. A split-plot in a randomized complete block design without replicates was used. The field was fertilized with 200 kg·ha−1 of ammonium nitrate (AN) and 250 kg·ha−1 of muriate of potash before sowing (basal fertilizer), followed with 250 kg·ha−1 AN for top dressing. In order to generate differential phosphorus conditions, 400 kg/ha of superphosphate fertilizer were added at pre-sowing to one half of the trial, corresponding to the optimum phosphorous fertilized conditions (OP). The other part of the trial corresponded to the non-phosphorus fertilized conditions (NPF). The trial was depleted of phosphorus for 1 year. A two-row border of a commercial maize variety was sown on the edges of the trial to prevent border effects. Trials were gathered following the standard procedures of CIMMYT. The central 3.5 m of each row was harvested discarding 2 plants at each end, thus the collected grain yield (t·ha−1) corresponded to the weight of 7.87 m2.
In addition, these hybrids were also tested in other trials in Zimbabwe under optimal fertilization conditions comparable to those of the OP trial of the experimental station. Evaluations were performed at the Agricultural Research Trust site in Harare (−17.716, 31.716, 1,516 masl). For these trials, the fertilization conditions were basically the same than at the OP conditions of the main study (CIMMYT Station).
Remote sensing evaluations were performed on seedlings (<5 leaves) during the last week of January. Vegetation indices derived from RGB images were evaluated for each plot at ground and aerial levels. At ground level one conventional digital picture was taken per plot, holding the camera about 80 cm above the plant canopy in a zenithal plane and focusing near the center of each plot. The digital camera used was an Olympus OM-D (Olympus, Tokyo, Japan). Pictures were acquired at a 16-megapixel resolution with a sensor using a 14-mm focal length, triggered at a speed of 1/125 s with the aperture programmed in automatic mode. NDVI was also determined on individual plots at ground level using a portable spectrometer (GreenSeeker handheld crop sensor, Trimble, USA). Additionally, the leaf chlorophyll content (LCC) of the last developed leaf was measured using a Minolta SPAD-502 portable chlorophyll meter (Spectrum Technologies Inc., Plainfield, IL, USA). Eight leaves were measured for each plot (four per row), each leaf being the last fully expanded within a plant. For each leaf four measurements were taken from the middle portion of the lamina.
Further, RGB and multispectral aerial images were acquired using an unmanned aerial vehicle (UAV) (Mikrokopters OktoXL, Moormerland, Germany) flying under remote control at around 50 m (Figure
RGB
To obtain correct image mosaics from the multispectral images a 3D reconstruction approach was needed to produce an accurate ortho-mosaic and remove the effects of the UAV flight. Agisoft PhotoScan Professional (Agi- soft LLC, St. Petersburg, Russia) was employed for this task using 20–30 overlapping images for both mosaics (RGB and multispectral) with at least 80% overlap. Through the open source image analysis platform FIJI (Fiji is Just ImageJ;
RGB pictures were subsequently analyzed using a version of the Breedpix 0.2 software adapted to JAVA8 and integrated as a plugin within FIJI;
Indices derived from the multispectral visible and near infrared bands.
Broadband greenness | Normalized difference vegetation index (NDVI) | (B840 – B670)/(B840 + B670) | Red, NIR | Rouse et al., |
Soil adjusted vegetation index (SAVI) | (B840 – B670)/(B840 + B670 + L)*(1 + L) | Red, NIR | Huete, |
|
Low vegetation, L = 1, intermediate, 0.5, and high 0.25 | ||||
Optimized soil-adjusted vegetation index (OSAVI) | ((1 + 0.16)*(B780 – B670))/((B780 + B670 + 0.16)) | Red, NIR | Rondeaux et al., |
|
Renormalized difference vegetation index (RDVI) | (B840 – B670)/((B840 + B670)∧1/2) | Red, NIR | Roujean and Breon, |
|
Enhanced vegetation index (EVI) | 2.5*(B840 – B670)/(B840 + (6*B670) − (7.5*B450) + 1) | Blue, Red, NIR | Huete et al., |
|
Light Use efficiency | Photochemical reflectance index (PRI) | (B550 – B570)/(B550 + B570) | Green | Gamon et al., |
Leaf pigments | Modified chlorophyll absorption ratio index (MCARI) | [(B700 – B670) – 0.2*(B700 – B550)]*B700/B670 | Green, Red | Daughtry, |
Transformed chlorophyll absorption in reflectance index (TCARI) | 3*(B700 – B670)-0.2*(B700 – B550)*(B700/B670) | Green, Red, NIR | Haboudane et al., |
|
Anthocyanin reflectance index 2 (ARI2) | B840*(1/B550 – 1/B700) | Blue, Red, NIR | Gitelson et al., |
|
Carotenoid reflectance index 2 (CRI2) | 1/B550 – 1/B700 | Blue, Red | Gitelson et al., |
|
Canopy water content | Water band index (WBI) | (R840 – B670)/(B840 + B670)∧(1/2) | Red, NIR | Peñuelas et al., |
Similar leaves to those used for leaf chlorophyll measurements were sampled and subsequently oven dried at 70°C for 24 h and ground to a fine powder. For the analysis of P content, a total of 100 mg of sample were digested in acid for 24 h at 90°C within Teflon vessels, using 2 ml of NHO3 and 0.5 ml of hydrogen peroxide, with samples subsequently re-suspended in 30 ml of deionized water. Analyses were performed by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) using a Perkin-Elmer Optima 3200RL Spectrometer (Perkin-Elmer, Massachusetts, EEUU) at the Scientific Facilities of the University of Barcelona. Leaf phosphorous content was expressed in mg of P per g of dry mass.
The same ground material was also used to analyze the total nitrogen content together with the stable isotopic abundances of carbon and nitrogen in the leaves. Samples of about 0.7 mg of dry matter and reference materials were weighed into tin capsules, sealed, and then loaded into an elemental analyzer (Flash 1112 EA; ThermoFinnigan, Schwerte, Germany) coupled with an isotope ratio mass spectrometer (Delta C IRMS, ThermoFinnigan), operating in continuous flow mode. Measurements were carried out at the Scientific Facilities of the University of Barcelona. The 13C/12C ratios (R) of plant material were expressed in composition (δ13C) notation (Coplen,
Where: sample refers to plant material and standard to Pee Dee Belemmite (PDB) calcium carbonate. International isotope secondary standards of a known 13C/12C ratio (IAEA CH7, polyethylene foil, IAEA CH6 sucrose and USGS 40 l-glutamic acid) were calibrated against Vienna Pee Dee Belemnite calcium carbonate (VPDB) with an analytical precision of 0.1%0. The 15N/14N ratios of plant material were also expressed in δ notation (δ15N) using international secondary standards of known 15N/14N ratios (IAEA N1 and IAEA N2 ammonium sulfate and IAEA NO3 potassium nitrate), with analytical precision of about 0.2%0. Further, the C/N ratio was obtained from these analyses.
Statistical analyses were conducted using the open source software, RStudio 1.0.44 (R Foundation for Statistical Computing, Vienna, Austria). Data for the set of physiological traits were subjected to factorial analyses of variance (ANOVAs) to test the effects of growing conditions on the different traits studied. A bivariate correlation procedure was used to calculate the Pearson correlation coefficients of the different remote sensing indices against the grain yield and the leaf phosphorus content. Multiple regressions were calculated via a forward stepwise method with GY and P content as dependent variables and the different indices as independent parameters. The figures were also drawn using the Rstudio software.
Omission of phosphorous fertilizer significantly decreased yield from a mean value (across genotypes) of 7.50 to 5.64 t ha−1 under optimum and no-phosphorous fertilizer conditions, respectively (Table
Effect of supplemental phosphorus fertilization on the grain yield (GY), leaf chlorophyll content (LCC), phosphorous content (P), leaf carbon and nitrogen concentration (C and N), leaf C/N ratio, and the stable carbon (δ13C) and nitrogen (δ15N) composition within the non-phosphorous fertilized (NPF) and the optimal phosphorous (OP) conditions.
GY (t ha−1) | 5.64 ± 0.20 | 7.5 ± 0.20 | 0.000 |
LCC | 32.01 ± 0.99 | 46.19 ± 0.78 | 0.000 |
P (mg/g DW) | 2.06 ± 0.08 | 4.81 ± 0.11 | 0.000 |
C (%) | 43.62 ± 0.10 | 43.03 ± 0.23 | 0.021 |
N (%) | 3.95 ± 0.04 | 4.30 ± 0.06 | 0.000 |
C/N | 11.08 ± 0.11 | 10.06 ± 0.13 | 0.000 |
δ13C (%0) | −11.66 ± 0.03 | −11.61 ± 0.04 | 0.428 |
δ15N (%0) | −1.32 ± 0.23 | −1.09 ± 0.30 | 0.541 |
The effect of phosphorous fertilization was also significant for the different leaf parameters studied. Thus, leaf total phosphorous content (P content) and chlorophyll content (LCC) strongly decreased in response to a lack of phosphorous fertilizer. The total nitrogen content (N) also decreased significantly (
Phosphorous-input also affected the RGB and multispectral indices (Table
Effect of phosphorous fertilization on remote sensing indices derived from RGB and spectral measurements within the non-phosphorous fertilized (NPF) and the optimal phosphorus (OP) conditions.
0.36 ± 0.00 | 0.36 ± 0.00 | 0.861 | |
30.63 ± 0.45 | 39.34 ± 1.23 | 0.000 |
|
0.19 ± 0.00 | 0.18 ± 0.00 | 0.000 |
|
42.35 ± 0.11 | 42.67 ± 0.25 | 0.243 | |
1.18 ± 0.15 | −1.93 ± 0.37 | 0.000 |
|
18.88 ± 0.23 | 18.48 ± 0.20 | 0.200 | |
10.82 ± 0.22 | 6.34 ± 0.49 | 0.000 |
|
ν |
20.38 ± 0.24 | 20.65 ± 0.26 | 0.440 |
0.08 ± 0.01 | 0.21 ± 0.01 | 0.000 |
|
0.08 ± 0.00 | 0.20 ± 0.01 | 0.000 |
|
0.50 ± 0.00 | 0.49 ± 0.00 | 0.003 |
|
23.53 ± 0.37 | 29.64 ± 0.72 | 0.000 |
|
0.24 ± 0.00 | 0.22 ± 0.00 | 0.000 |
|
55.13 ± 0.25 | 53.94 ± 0.40 | 0.014 |
|
9.39 ± 0.22 | 4.42 ± 0.42 | 0.000 |
|
26.53 ± 0.22 | 25.18 ± 0.23 | 0.000 |
|
28.05 ± 0.34 | 19.54 ± 0.69 | 0.000 |
|
ν* | 28.28 ± 0.24 | 27.82 ± 0.25 | 0.192 |
0.07 ± 0.01 | 0.20 ± 0.01 | 0.000 |
|
0.02 ± 0.00 | 0.12 ± 0.01 | 0.000 |
|
0.30 ± 0.03 | 0.49 ± 0.03 | 0.000 |
|
0.35 ± 0.01 | 0.50 ± 0.01 | 0.000 |
|
0.16 ± 0.01 | 0.24 ± 0.01 | 0.000 |
|
0.23 ± 0.01 | 0.34 ± 0.01 | 0.000 |
|
0.16 ± 0.00 | 0.25 ± 0.01 | 0.000 |
|
0.22 ± 0.01 | 0.35 ± 0.01 | 0.000 |
|
0.16 ± 0.01 | 0.18 ± 0.00 | 0.001 |
|
0.05 ± 0.04 | 0.06 ± 0.00 | 0.000 |
|
0.08 ± 0.00 | 0.09 ± 0.00 | 0.012 |
|
0.36 ± 0.01 | 0.26 ± 0.01 | 0.000 |
|
0.75 ± 0.02 | 0.67 ± 0.02 | 0.010 |
|
6.65 ± 0.12 | 6.03 ± 0.20 | 0.009 |
|
0.92 ± 0.00 | 0.94 ± 0.01 | 0.000 |
The multispectral index NDVI also decreased significantly (
Correlations between the remote sensing indices Hue, a*, u*, GA, GGA, and NDVI assessed at ground level against the same indices measured from the UAV were very strong (Table
Regression coefficients (r) of the relationships between the remote sensing indices measured at ground against the same VIs measured at aerial level.
0.275 | 0.000 |
|
0.902 |
0.000 |
|
0.466 | 0.000 |
|
0.126 | 0.000 |
|
0.919 |
0.000 |
|
0.316 | 0.000 |
|
0.903 |
0.000 |
|
ν | 0.310 | 0.000 |
0.970 |
0.509 | |
0.942 |
0.000 |
|
0.889 |
0.000 |
Correlation coefficients for the relationships of grain yield with both the RGB (Table
Regression coefficients of the relationships between the RGB-indices, measured at ground and aerial levels, with grain yield and P content.
0.194 | −0.217 | −0.084 | −0.014 | −0.067 | −0.041 | |
0.777 |
0.732 |
0.827 |
0.336 | −0.370 | 0.594 |
|
0.468 |
−0.027 | −0.179 | 0.065 | 0.247 | −0.429 |
|
0.459 |
−0.014 | 0.205 | 0.086 | −0.152 | 0.126 | |
−0.601 |
−0.725 |
−0.818 |
−0.334 | 0.405 |
−0.643 |
|
0.572 |
0.226 | 0.171 | 0.110 | −0.020 | −0.157 | |
−0.300 | −0.729 |
−0.786 |
−0.267 | 0.425 |
−0.667 |
|
ν |
0.642 |
0.362 | 0.434 |
0.151 | −0.152 | 0.094 |
0.816 |
0.817 |
0.878 |
0.111 | −0.369 | 0.707 |
|
0.822 |
0.816 |
0.877 |
0.122 | −0.367 | 0.711 |
|
−0.223 | −0.715 |
−0.620 |
0.166 | 0.021 | −0.359 | |
0.731 |
0.798 |
0.868 |
−0.062 | −0.361 | 0.624 |
|
0.149 | 0.266 | −0.235 | −0.539 |
−0.112 | −0.581 |
|
−0.102 | −0.653 |
−0.526 |
0.109 | −0.047 | −0.316 | |
−0.856 |
−0.784 |
−0.883 |
−0.284 | 0.339 | −0.750 |
|
0.192 | 0.002 | −0.292 |
−0.466 |
−0.221 | −0.575 |
|
−0.830 |
−0.777 |
−0.873 |
−0.424 |
0.284 | −0.777 |
|
ν |
0.318 | 0.084 | 0.016 | −0.333 | −0.337 | −0.283 |
0.837 |
0.814 |
0.891 |
0.139 | −0.343 | 0.693 |
|
0.790 |
0.752 |
0.837 |
0.206 | −0.309 | 0.697 |
Regression coefficients of the relationships between the multispectral-indices and the multispectral with grain yield, P and N content.
0.734 |
0.711 |
0.863 |
0.058 | −0.423 |
0.669 |
|
0.628 |
0.643 |
0.823 |
0.324 | −0.347 | 0.800 |
|
0.652 |
0.644 |
0.823 |
0.159 | −0.269 | 0.790 |
|
OSAVI | 0.657 |
0.655 |
0.829 |
0.216 | −0.303 | 0.797 |
0.658 |
0.650 |
0.829 |
0.198 | −0.286 | 0.795 |
|
0.613 |
0.529 |
0.798 |
0.119 | −0.220 | 0.782 |
|
0.039 | 0.312 | 0.406 |
0.428 |
0.032 | 0.466 |
|
0.358 | −0.019 | 0.452 |
−0.035 | −0.033 | 0.463 |
|
TCARI | 0.172 | −0.200 | 0.238 | −0.147 | 0.055 | 0.314 |
TCARI/OSAVI | −0.401 |
−0.618 |
−0.748 |
−0.368 | 0.283 | −0.700 |
−0.012 | 0.286 | −0.133 | −0.286 | −0.002 | −0.363 | |
0.016 | 0.359 | −0.091 | −0.162 | −0.064 | −0.364 | |
0.241 | 0.595 |
0.598 |
−0.014 | −0.064 | 0.414 |
|
B450 | −0.348 | −0.688 |
−0.638 |
−0.459 | 0.318 | −0.383 |
B550 | 0.261 | −0.505 |
−0.102 | −0.205 | 0.371 | 0.036 |
B570 | 0.032 | −0.529 |
−0.419 |
−0.498 |
0.212 | −0.354 |
B670 | −0.302 | −0.566 |
−0.739 |
−0.540 |
0.398 | −0.731 |
B700 | −0.116 | −0.525 |
−0.602 |
−0.463 |
0.324 | −0.567 |
B720 | 0.269 | −0.045 | 0.153 | −0.319 | 0.125 | 0.047 |
B780 | 0.465 |
0.477 |
0.741 |
−0.020 | −0.122 | 0.688 |
B840 | 0.496 |
0.550 |
0.779 |
0.010 | −0.137 | 0.744 |
B860 | 0.442 |
0.492 |
0.753 |
−0.051 | −0.129 | 0.736 |
B900 | 0.425 |
0.537 |
0.761 |
−0.063 | −0.083 | 0.739 |
B950 | 0.390 |
0.411 |
0.724 |
−0.024 | −0.091 | 0.741 |
Concerning NDVI, and regardless the fertilization level, the highest correlation with GY was found with ground spectroradiometer measurements, although the NDVI derived from the UAV was still highly correlated with GY (Table
For the purpose of testing how the combination of different indices measured from the aerial platform may improve the strength and accuracy of the assessment of grain yield and phosphorous concentration, stepwise regressions were performed (Table
Multilinear regression (stepwise) of grain yield (GY) as dependent variable and the different categories of remote sensing traits (RGB VIs, multispectral VIs, and specific multispectral bands) measured from the unmanned aerial vehicle within the non-phosphorus fertilization (NPF) and the optimal phosphorus (OP) trials.
GY | Aerial RGB VIs | 0.821 | 0.590 | 0.000 | |||
Multispectral VIs | 0.463 | 0.769 | 0.000 | ||||
Aerial RGB VIs | 0.662 | 0.596 | 0.000 | ||||
Multispectral VIs | 0.652 | 0.632 | 0.000 | ||||
P content | Aerial RGB VIs | 0.436 | 0.337 | 0.001 | |||
Multispectral VIs | 0.311 | 0.381 | 0.038 | ||||
Aerial RGB VIs | 0.210 | 0.520 | 0.065 | b* = 0.34 | |||
v* = 0.65 | |||||||
Multispectral VIs | 0.151 | 0.539 | 0.150 | ||||
In order to check the ability of the remote sensing indices to predict genotypic differences in yield, we correlated the genotypic values of the different categories of remote sensing traits evaluated in the seedlings with the yield of each hybrid determined from multi-location trials developed in parallel (Table
Regression coefficients (r) of the relationships across the genotypes of the VI's measured in seedlings at non-phosphorus fertilization (NPF) and optimal phosphorous (OP) conditions in this study against grain yield data from other trials.
0.079 | −0.237 | |
0.494 |
0.695 |
|
0.562 |
−0.039 | |
0.311 | −0.047 | |
−0.232 | −0.677 |
|
0.592 |
0.187 | |
0.057 | −0.685 |
|
ν |
0.602 |
0.314 |
0.738 |
0.830 |
|
0.741 |
0.828 |
|
−0.465 |
−0.643 |
|
0.767 |
0.766 |
|
0.491 |
0.360 | |
−0.317 | −0.570 |
|
−0.705 |
−0.721 |
|
0.423 |
0.137 | |
−0.625 |
−0.692 |
|
ν |
0.450 |
0.209 |
0.848 |
0.779 |
|
0.785 |
0.730 |
|
NDVI g | 0.752 |
0.594 |
NDVI | 0.656 |
0.629 |
−0.207 | 0.223 | |
0.658 |
0.630 |
|
MCARI | 0.399 |
−0.017 |
0.486 |
0.573 |
|
RDVI | 0.721 |
0.630 |
0.403 |
0.334 | |
0.133 | 0.162 | |
0.112 | 0.243 | |
0.304 | −0.157 | |
0.552 |
0.611 |
Phosphorous is an essential nutrient for plant growth and development (Manschadi et al.,
Yield variations caused by differences in the water status of the plants can be ruled out through the lack of differences in δ13C. Even for a C4 plant like maize, differences in plant water status, and intrinsic photosynthetic metabolism may be reflected in the δ13C of the plant matter, with δ13C decreasing in response to water stress (Farquhar et al.,
Phosphorous and nitrogen content in the leaves correlated within each fertilization levels (Supplementary Figure
The vegetation indices derived from conventional digital RGB images have been proposed as a means of estimating green biomass and grain yield in maize and other cereals under stress conditions (Ahmad and Reid,
Examples of the differences in resolution between images taken at ground level and aerially.
The RGB-based indices, GA and GGA, were the best at GY prediction, outperforming other RGB indices, NDVI and the rest of the spectral indices. Considering that the data of our study was collected at an early phenological stage, the plants were not able to cover the soil completely. Therefore, the superior performance of these indices should be attributable, at least in part, to their insensitivity to soil color (Casadesús et al.,
A recent study has concluded that RGB images performed better than NDVI in determining genotypic differences in hybrid maize yield under different nitrogen fertilization conditions (Vergara-Díaz et al.,
MCARI is an index that measures the depth of chlorophyll absorption at 670 nm relative to the reflectance at 550 and 700 nm (Daughtry,
The complementary metal-oxide-semiconductor (CMOS) image sensor of the micro-MCA12 camera is optimized to collect wavelengths at ~800 nm, dropping in a smooth curve to a low relative efficiency at 400 nm in the visible wavelengths and a smaller reduction in efficiency at 1050 nm in the NIR, at the limits of its range. As a consequence, the efficiency of the measurements in the blue band (450 nm) is considerably lower (20%) in comparison to the measurements of the NIR or the R bands (85% both). Due to this limitation in the blue region sensitivity, more noise is included in the measurements of the blue band. Moreover, inadequate phosphorus content can result in a darkening of the leaves to a purple color. This would explain why the single band measurement in the blue region correlated with GY at optimum conditions but it failed to do so under non-fertilized conditions. The correlation analysis between each multispectral band and yield has identified sensitive wavelengths under both phosphorus levels, and this ranges from 780 to 950 nm of the near-infrared (NIR, 750–1,350 nm) region of the spectrum.
The results obtained proved that measurements at an early growing date, while the plants are still seedlings, are optimal for the assessment of the future yield.
The strength of the correlations inside each treatment between the indices and the P content were far lower than of these indices with GY. Distribution of values is not uniform and in fact the linear correlation has not any sense besides to show these vegetation indices are able to clearly differentiate between the two different groups of phosphorous fertilization (but not across genotypes within each fertilization level). The same happened with the LCC and the leaf nitrogen content (Supplementary Figure
Similarly, the multispectral indices didn't show significant correlations with P content within each fertilization level, while several of these indices correlated with GY. Only the PRI correlated with leaf phosphorous content (and just under low P conditions). The PRI is a spectral index increasingly used as an indicator of photosynthetic efficiency because it is based on the short-term reversible xanthophyll pigment cycle (Peñuelas et al.,
There is a need for phenotyping tools which increase the selection efficiency and to understand mechanisms of phosphorous tolerance. This study clearly shows a genotypic variability for low phosphorous tolerance, with a reduction in yields of 25% in average in comparison with the optimum conditions. Previous studies in the literature suggests that only when reduction is 75% or more, selecting for specific adaptation to tolerance to low nutrient availability is the strategy (Bänziger et al.,
This study emphasizes the capabilities of RGB vegetation indices as phenotypic traits for predicting maize performance during early stages of crop growth. GA was the vegetation index best correlated with grain yield across maize hybrids and regardless the phosphorous fertilization level and therefore this index may serve to select the most productive hybrids for the SSA. RGB indices assessed at ground level were comparable to those measured from an aerial platform. Moreover, RGB indices performed better than multispectral vegetation indices. The use of vegetation indices derived from RGB images may represent a very affordable approach for phenotyping and may become even more economical due to the similarity between results obtained from ground evaluation and those achieved from aerial platforms. The phenotypic correlations found between the remote sensing indices of seedlings and the genotypic yield data collected in the multi-location trials confirm their usefulness. Despite its comparatively low tech and low-cost nature, digital photography is a promising approach, and its derived indices have demonstrated potential for the assessment of crop management in maize, making it ideal for developing countries in particular.
Additionally, RGB-derived vegetation indices are also amenable for monitoring the effects of phosphorous fertilizer applications. However, only some of the indices best correlated with grain yield exhibited significant, albeit weaker, correlations with leaf phosphorus content. Moreover, these correlations were only present under low phosphorus fertilization, which suggests that they were linked to differences in biomass and grain yield caused by phosphorous fertilization.
MZ-A, BP, and JC managed and directed the maize trials at the Southern Africa regional office of CIMMYT in Harare, Zimbabwe. SK carried out the UAV flights for the obtainment of aerial measurements. OV-D and JA conducted the field measurements and the collection of samples. AG-R processed the images, analyzed the samples and wrote the paper under the supervision of JA and SK and with the contributions from all the other authors.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank the personnel from the CIMMYT Southern Africa Regional Office at Harare for their support during the field measurements and sampling. The trials were planted under the Bill and Melinda Gates funded project, Improved Maize for African Soils. Finally, we thank Dr. Jaume Casadesús for providing the BreedPix software.
The Supplementary Material for this article can be found online at:
Sub-Saharan Africa
Red-Blue-Green
Normalized Difference Vegetation Index
Unmanned aerial Vehicle
Grain yield
Vegetation Indices
Hue-Intensity-Saturation
Green Area
Greener Area
Ammonium Nitrate
International Maize and Wheat Improvement Center
meters above sea level
Inductively Coupled Plasma Optical Emission Spectroscopy
Phosphorous content
Chlorophyll Content
Photochemical Reflectance Index
Soil Adjusted Vegetation Index
Modified Chlorophyll Absorption Ratio Index
Water Band Index
Renormalized Difference Vegetation Index
Enhanced Vegetation Index
Anthocyanin Reflectance Index 2
Carotenoid Reflectance Index 2
Transformed Chlorophyll Absorption in Reflectance Index
Optimized Soil-Adjusted Vegetation Index
Effective Fluorescence Quantum yield
Near-Infrared.