Phenomic selection and prediction of maize grain yield from near‐infrared reflectance spectroscopy of kernels

Lane, Holly M.; Murray, Seth C.; Montesinos‐López, Osval A.; Montesinos‐López, Abelardo; Crossa, J.; Rooney, David K.; Barrero-Farfan, Ivan D.; Fuente, Gerald N. De La; Morgan, Cristine L.S.

doi:10.1002/ppj2.20002

Cited by 51 publications

(103 citation statements)

References 52 publications

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“…Several studies reported the strong correlation between reflectance bands and yield in different crop plants such as alfalfa ( Kayad et al, 2016 ; Feng et al, 2020 ), wheat ( Prey and Schmidhalter, 2019 ), maize ( Lane et al, 2020 ), rice ( Wan et al, 2020 ), and sugarcane ( Verma et al, 2020 ). The visible reflectance bands can be splitted into three main regions, red (650–700 nm), green (495–570 nm), and violet–blue (390–495 nm) ( Hennessy et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Yoosefzadeh-Najafabadi

et al. 2021

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Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble–stacking (E–S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E–S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E–S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E–S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

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Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Yoosefzadeh-Najafabadi

et al. 2021

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“…Some compounds with similar biosynthetic origins do not share strong correlations. For example, fat, crude fiber and starch are both made from glucose, yet they are negatively correlated ( Figure 3 ), and Lane et al [ 47 ] also reported a very poor correlation between starch and fat (r = <0.17). This negative correlation is likely due to the very different functional roles played by the compounds.…”

Section: Discussionmentioning

confidence: 99%

Compositional Analyses Reveal Relationships among Components of Blue Maize Grains

Nankar

Scott

Pratt

2020

Plants

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One aim of this experiment was to develop NIR calibrations for 20-grain components in 143 pigmented maize samples evaluated in four locations across New Mexico during 2013 and 2014. Based on reference analysis, prediction models were developed using principal component regression (PCR) and partial least squares (PLS). The predictive ability of calibrations was generally low, with the calibrations for methionine and glycine performing best by PCR and PLS. The second aim was to explore the relationships among grain constituents. In PCA, the first three PCs explained 49.62, 22.20, and 6.92% of the total variance and tend to align with nitrogen-containing compounds (amino acids), carbon-rich compounds (starch, anthocyanin, fiber, and fat), and sulfur-containing compounds (cysteine and methionine), respectively. Correlations among traits were identified, and these relationships were illustrated by a correlation network. Some relationships among components were driven by common synthetic origins, for example, among amino acids derived from pyruvate. Similarly, anthocyanins, crude fat, and fatty acids all share malonyl CoA in their biosynthetic pathways and were correlated. In contrast, crude fiber and starch have similar biosynthetic origins but were negatively correlated, and this may have been due to their different functional roles in structure and energy storage, respectively.

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“…One method to achieve HT analysis of quinoa seed composition is the use of near-infrared (NIR) spectroscopy, in which, in theory, any sample measurement can be predicted as long as the sample’s spectral data are correlated with the desired measurement [ 197 ]. This technology has diverse capabilities, such as phenomic selection and prediction of maize yield from kernels [ 198 ], and has been successfully applied to predict amino acid content in quinoa [ 199 ]. In addition to the NIR methodology, mid-infrared should be considered as a useful technology with practical applications in quinoa.…”

Section: Harvest and Post-harvestmentioning

confidence: 99%

Quinoa Phenotyping Methodologies: An International Consensus

Stanschewski¹,

Rey²,

Fiene³

et al. 2021

Plants

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Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

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Phenomic selection and prediction of maize grain yield from near‐infrared reflectance spectroscopy of kernels

Cited by 51 publications

References 52 publications

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Compositional Analyses Reveal Relationships among Components of Blue Maize Grains

Quinoa Phenotyping Methodologies: An International Consensus

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