Near-infrared spectroscopy used to predict soybean seed germination and vigour

AL-Amery, Maythem; Geneve, Robert L.; Sanches, Maurício Feis Ganz; Armstrong, Paul R.; Maghirang, Elizabeth B.; Lee, Chad; Vieira, Roberval Daiton; Hildebrand, David F.

doi:10.1017/s0960258518000119

Cited by 27 publications

(18 citation statements)

References 32 publications

(40 reference statements)

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“…The results showed that the AdaBoost algorithm could greatly improve the classification performance of the original model. Compared with the current related study using NIR or HSI technology (Al‐Amery et al, ; Zhang et al, ), FHSI technology combined with the optimization model reduced the data dimension of the model and improved the discrimination accuracy of viable and nonviable seeds. In summary, the developed model CARS‐SVM‐AdaBoost not only had a satisfactory classification effect, but also had good stability of the model.…”

Section: Resultsmentioning

confidence: 94%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

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In this study, the feasibility of the fluorescence hyperspectral imaging (FHSI) technology to detect the viability of soybean seeds was investigated. Viable and nonviable seed samples were obtained by artificial aging method. Hyperspectral images of samples were collected by the FHSI device and then the spectral data were collected. Characteristic wavelengths were respectively selected by three variable selection methods, eliminating a large number of redundant information irrelevant to the viability of soybean seeds. Support vector machine (SVM) models based on the full spectra and the optimal spectral data were developed to identify the viability of soybean seeds. To further improve the accuracy of the model, the adaptive boosting (AdaBoost) algorithm was used. The results showed that the accuracy of the calibration and validation sets in the CARS‐SVM‐AdaBoost model (22 characteristic wavelengths) reached 100%, indicating that the combination of FHSI technology and the optimization model can greatly improve the recognition accuracy. Practical applications A rapid and accurate nondestructive identification method of viability of soybean seeds can contribute to the construction of the online seed viability detection system. FHSI technology has the advantages of high sensitivity and comprehensive analysis of sample information. Combined with the optimization model proposed in this paper, the recognition accuracy can be greatly improved. It can be applied to the online seed viability detection by seed companies, seed quality inspection departments, and soybean breeding units.

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Section: Resultsmentioning

confidence: 94%

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Sun

et al. 2019

J Food Process Preserv

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“…Standard germination percentages provide an estimation of a seed lot's potential for germination and seedling establishment under favourable conditions. Amery et al, (2018) reported that differences in spectral absorbance were observed for low and high germinating seed lots in soybean (Fig 1). This was observed over the entire 950-1650 nm wavelength range with more pronounced differences found beyond 1400 nm, indicating the potential to differentiate between seed lot categories using NIR spectroscopy.…”

Section: Nir-based Prediction Of Seed Germinationmentioning

confidence: 93%

Role of Near - Infrared Spectroscopy in Seed Quality Evaluation: A Review

Masilamani¹,

Venkatesan²,

Janaki³

et al. 2020

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The use of high-quality seeds is one of the most important elements for increasing agricultural production in any farming system. This element has become more crucial than ever for providing enough food security for the rising population, which is expected to exceed nine billion by year 2050. Selecting high yielding varieties of disease, insect, lodging and shattering resistance, along with other desirable characteristics are the basic keys for satisfactory crop performance and yield. The production of high-quality seed is the cornerstone of any successful agriculture program. It is also a good marketing tool for increasing the potential sale of crops, especially in today’s competitive market. Therefore, adopting an efficient method to evaluate the seed quality non-destructively is the need of hour. One such technique or method is the use of NIR which helps to assess seed quality non-destructively and sort out seeds based on seed health, seed deterioration, viability, vigour including protein, starch and fatty acid composition as well as abiotic and biotic seed damage. It is a non-destructive analytical technique requires little sample preparation time and high-throughput, which makes it as a seed analysis tool.

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“…Seed vigor is another important trait that influences rapid and uniform development of seedlings under variable field conditions (Sheidaei, Abad, Hamidi, Mohammadi, & Moghaddam, 2014;Tekrony & Egli, 1977). Quantitative measurement of NIR-spectra successfully categorized soybean seeds into low and high vigor seed lots with 80-100% and 96% accuracy, respectively, thus suggesting that NIRS-based predictive models can be used to characterize commercial seed lots for seed vigor (Al-Amery et al, 2019). The relative content of seed protein (RCP, %), as detected by near-infrared spectrum combining with seed weight, could be used for rapid and nondestructive determination of absolute content of protein (ACP, mg seed -1 ) in seed.…”

Section: High Throughput Phenotyping and Software To Monitor Variatiomentioning

confidence: 99%

First the seed: Genomic advances in seed science for improved crop productivity and food security

Dwivedi¹,

Spillane

Lopez

et al. 2021

Crop Science

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Seeds are valuable sources of carbohydrates, lipids, proteins, fibers, minerals, and vitamins. They provide energy and nutrition to germinating seedlings, food to humans, feed to livestock and feedstocks to industry. High-throughput analyses of gene expression in crops has identified many candidate genes associated with seed dormancy, longevity, germination, and vigor. In this review, we cover the latest research focusing on such key seed traits. Transcriptome analyses of time-courses of seed filling have identified sets of genes expressed at different stages of this process. The potential role of epigenetics (including seed-endosperm imprinted genes) in regulating seed development and chemistry is highlighted herein. We also discuss how advances in genomics and seed biology are facilitating the unravelling of associations between seed traits with genebank accessions and gene sequences, including how functional research can accelerate the discovery of allelic variants. Such knowledge of functional effects relating to gene variants is necessary for more efficient and cost-effective management of genetic resources or for redesigning crops with specific seed characteristics. For instance, genebank curators may assess seed viability by monitoring changes in gene expression of biomarker genes in dry seed samples to decide germplasm regeneration and assess genetic integrity of collections by monitoring changes in This article is protected by copyright. All rights reserved. diversity and allele frequencies between samples of same accession stored in genebanks. We highlight that resistance to preharvest sprouting can be enhanced through genomicsassisted breeding in otherwise nondormant rice and wheat cultivars, while pimt, another valuable marker for seed longevity, may be deployed to enhance seed vigor in crops.

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Near-infrared spectroscopy used to predict soybean seed germination and vigour

Cited by 27 publications

References 32 publications

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Role of Near - Infrared Spectroscopy in Seed Quality Evaluation: A Review

First the seed: Genomic advances in seed science for improved crop productivity and food security

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