Estimation of grain yield by near-infrared reflectance spectroscopy in durum wheat

Ferrio, Juan Pedro; Bertran, E.; Nachit, M. M.; Català, Jordi; Araus, José Luís

doi:10.1023/b:euph.0000040523.52707.1e

Cited by 17 publications

(13 citation statements)

References 25 publications

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“…One recurrent question surrounding NIRS prediction methods are the optimization of the environments in which to capture scans. Spectra collection in environments separate from the environment in which calibrations are made has presented challenges in the past (Ferrio et al., 2004), and remains a key question to be answered in optimizing selection models using NIRS. It has been shown that NIRS can be used to predict in new environments (Rincent et al., 2018), but understanding what makes the best calibration site is yet to be determined.…”

Section: Discussionmentioning

confidence: 99%

“…However, NIRS has been previously shown to correlate with yield. Ferrio, Bertran, Nachit, Català, and Araus (2004) investigated grain yield correlation with NIRS of durum wheat ( Triticum aestivum L.) flour. Despite high r values, the slopes of their predicted and measured yield values were not in unity, causing them to conclude that the ability of NIRS to predict grain yield in wheat was not an accurate way to achieve estimations under their conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Lane

Murray

Montesinos‐López

et al. 2020

The Plant Phenome Journal

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High-throughput phenotyping technologies, which can generate large volumes of data at low costs, may be used to indirectly predict yield. We explore this concept, using high-throughput phenotype information from Fourier transformed near-infrared reflectance spectroscopy (NIRS) of harvested kernels to predict parental grain yield in maize (Zea mays L.), and demonstrate a proof of concept for phenomic-based models in maize breeding. A dataset of 2,563 whole-kernel samples from a diversity panel of 346 hybrid testcrosses were scanned on a plot basis using NIRS. Scans consisted of 3,076 wavenumbers (bands) in the range of 4,000-10,000 cm −1. Corresponding grain yield for each sample was used to train phenomic prediction and selection models using three types of statistical learning: (a) partial least square regression (PLSR), (b) NIRS best linear unbiased predictor (NIRS BLUP), and (c) functional regression. Our results found that NIRS data were a useful tool to predict maize grain yield and showed promising results for evaluating genetically independent breeding populations. All model types were successful; functional regression followed by the PLSR model resulted in the best predictions. Pearson's correlations between predicted and observed grain yields exceeded .7 in many cases within random cross validation. Abbreviations: AF, aflatoxin; BLUE, best linear unbiased estimator; BLUP, best linear unbiased predictor; CV, cross validation; CV0, predicting one environment using data from all other environments; CV1, 20% of the hybrids are predicted by the remaining 80% of hybrids (five-fold), within each environment; CV2, predicting across environments, where hybrids are seen in some environments but predicted in others (mimics sparse testing); G × E, genotype by environment; G-BLUP, genomic best linear unbiased predictor; GEM, germplasm enhancement of maize lines; GWAS, genome-wide association study; LM, simple linear model; NIRS, near-infrared reflectance spectroscopy; NIRS BLUP, NIRS-based best linear unbiased predictor; PLSR, partial least squares regression; RMSEP, root mean square error of prediction; SERAT, southeast regional aflatoxin trial; UAS, unoccupied aerial systems; WS, water stress, unirrigated treatment; WW, well-watered, irrigated treatment. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lane

Murray

Montesinos‐López

et al. 2020

The Plant Phenome Journal

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“…With increasing wavelengths of up to 2500 nm, the reflectance decreases gradually because of increased absorption by the water present in the leaves [ 33 , 132 ]. Near infrared spectroscopy for the indirect assessment of crop growth and yield performance under potential yield and stress conditions has been addressed more recently [ 133 ]. Spectral reflectance information from leaves or canopies is used to quantify vegetation indices, which are simple operations (e.g., ratios and differences) between spectral reflectance data at given wavelengths.…”

Section: Key Imaging Techniques In Plant Phenotypingmentioning

confidence: 99%

A Review of Imaging Techniques for Plant Phenotyping

Zhang

Huang

2014

Sensors

868

598

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Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

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“…Recently, the scope of NIRS has been expanded to include the determination of the physiological indices of crops (Schmilovitch et al 2000) or the optimal picking date of fruits (Peirs et al 2000). Ferrio et al (2004) reported the expanded usage of NIRS for ranking grain yield during the early generations of the durum wheat breeding program. Biochemical indicators based on the content of various chemical components have also been developed to determine the physiological age of potato tubers (Caldiz et al 2001).…”

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confidence: 99%

Prediction of Sprouting Capacity Using Near-infrared Spectroscopy in Potato Tubers

Jeong

Kim

2008

Am. J. Pot Res

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The potential of near infra-red spectroscopy (NIRS) for predicting the sprouting capacity of potato (Solanum tuberosum L.) tubers was investigated. The experiment was conducted for 2 years using 'Superior' and 'Atlantic' potatoes. A total of 380 potato tubers (200, Superior; 180, Atlantic) weighing 100-200 g were scanned by NIRS, and the sample sets were divided into four groups; three groups were classified based on the cultivars or years as follows: 'Superior', 2004; 'Superior', 2005; 'Atlantic', 2005; the fourth group contained the total number of samples. The reference value for the sprouting capacities was measured by determining the sprout weight after 30 days of incubation at 20°C. The first derivative transformation equation for raw optical data was standardized by applying standard normal variate (SNV) and detrending (DT) algorithms, and calibration equations between them were developed by modified partial least square (MPLS) regression. The coefficient of determination (r 2 ) ranged from 0.868 to 0.965 with a standard error of calibration (SEC) from 0.31 to 0.40 for the calibration sets; r 2 ranged from 0.724 to 0.904 with a standard error of prediction (SEP) from 0.532 to 0.602 for the four validation sets. These results indicate that the calibration model obtained from this experiment is applicable to all the different cultivars for different years, and the NIRS method is a powerful tool for the predictive assessment of the sprouting capacity of potato tubers.

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Estimation of grain yield by near-infrared reflectance spectroscopy in durum wheat

Cited by 17 publications

References 25 publications

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

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

A Review of Imaging Techniques for Plant Phenotyping

Prediction of Sprouting Capacity Using Near-infrared Spectroscopy in Potato Tubers

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