Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes

Weber, V.S.; Araus, José Luís; Cairns, Jill E.; Sánchez, C.; Melchinger, Albrecht E.; Orsini, Elena

doi:10.1016/j.fcr.2011.12.016

Cited by 153 publications

(120 citation statements)

References 30 publications

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“…These results indicate the method's sensitivity for capturing small spectral variations in genotypes exposed to extreme environmental conditions. Similar results were found by Weber et al [24] and Ferrio et al [25], who used methods from multivariate regressions and who also demonstrated results that were more robust than conventional spectral indices. Both studies used the PLSR method and generated predictions with coefficients of determination (R 2 ) between 16% and 76% [25] and 69% and 71% [24], which were lower values than the ridge regression model used with our data set.…”

Section: Comparison Between the Spectral Vegetation Indices And Ridgesupporting

confidence: 78%

“…These differences are not considered when generating the prediction because an empirical model is being used. A similar situation was observed by Weber et al [24] in maize where a decrease in the capacity to predict GY was found when using regression coefficients of combined models in the environments that generated them.…”

Section: Grain Yield Prediction Using Multivariate Modelssupporting

confidence: 54%

“…However, these indices only use a limited portion of the spectral information by reducing, simplifying, or not considering the abundant information it contains [22,23]. Also, optimal wavelengths for estimating traits, such as GY, can vary in accordance with the phenological stage and environmental conditions [18,24].…”

Section: Introductionmentioning

confidence: 99%

“…[23][24][25]. One of the approaches is a linear regression model based on least squares; however, this technique performs poorly when input variables are collinear or exceed the number of training cases [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…According to Ferrio et al [25], this type of approach should be proven empirically in the field to understand the indirect relationship between wavelengths and GY because it can vary depending on the environmental conditions and location. In durum wheat [25] and maize [24], GY prediction has been evaluated by multivariate techniques using partial least square regression (PLRS). This technique breaks down all the variability of spectral information into a few factors that determine GY and leads to correct results.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

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Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg·ha −1 , respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY OPEN ACCESSRemote Sens. 2015, 7 2110 variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R 2 ) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

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Section: Comparison Between the Spectral Vegetation Indices And Ridgesupporting

confidence: 78%

Section: Grain Yield Prediction Using Multivariate Modelssupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Hernandez

Lobos

Matus³

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

Water stress tolerance in maize

Chellamma¹,

Pillai²

2016

Water Stress and Crop Plants

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Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

Galli

Horne

Collins

et al. 2020

The Plant Phenome Journal

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High-throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high-throughput traits could lead to significant optimization of HTP-assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS-based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75; and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health; 104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field-based breeding program and suggest additional optimization options. Abbreviations: AUDPC, area under the disease progress curve; CIg, chlorophyll index; CIre, red-edge chlorophyll index; DAS, days after sowing; GY, grain yield; HTP, high-throughput phenotyping; ML, machine learning; MSR, modified simple ratio; MSRre, red-edge modified simple ratio; NDVI, normalized difference vegetation index; NDVIre, red-edge normalized difference vegetation index; PLSR, partial least squares regression; UAS, unmanned aerial system. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes

Cited by 153 publications

References 30 publications

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Water stress tolerance in maize

Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum

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