Improving Head Rice Yield and Milling Quality: State-of-the-Art and Future Prospects

Butardo, Vito M.; Sreenivasulu, Nese

doi:10.1007/978-1-4939-8914-0_1

Cited by 18 publications

(16 citation statements)

References 79 publications

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“…Unravelling the novel genetic variants influencing low chalkiness in rice that were identified using a GWAS and epistatic interactions Farmers sell their harvested paddies to millers, who avoid harvests with high levels of chalky rice grains, as they are predisposed to breakage during the milling and polishing processes (Butardo and Sreenivasulu, 2019). Thus, any undesirable traits, such as an increase in chalkiness that directly affects the premium, need to be selected against by the breeders in developing varieties.…”

Section: Discussionmentioning

confidence: 99%

“…Rice ( Oryza sativa L.) is one of the oldest domesticated crops in Asia, originating from the wild rice species O. rufipogon . It currently contributes to the daily energy needs of half of the world’s population (Butardo and Sreenivasulu, 2019). The two major subspecies of rice are japonica , which has short, bold grains with either a sticky or soft texture, and indica which has medium or long slender non‐sticky grains with a fluffy texture (Misra et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Rice traders consider milled seed lots with less than 2% chalky grains to be premium, 5% chalkiness is considered grade 1, 10% chalkiness as grade 2, and seed lots with more than 15% chalkiness are grade 3 and have the lowest market value (Laborte et al ., 2015). Chalkiness is considered undesirable in part, because it has a negative impact on the cooking quality of the rice (Butardo and Sreenivasulu, 2019). Overall, this undesirable trait has a negative influence on consumer acceptability and crop market value (Laborte et al ., 2015; Siebenmorgen et al ., 2013).…”

Section: Introductionmentioning

confidence: 99%

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Genome‐wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness

Misra

Badoni

Parween

et al. 2020

Plant Biotechnology Journal

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Rice varieties whose quality is graded as excellent have a lower percent grain chalkiness (PGC) of two per cent and below with higher whole grain yields upon milling, leading to higher economic returns for farmers. We have conducted a genome-wide association study (GWAS) using a combined population panel of indica and japonica rice varieties, and identified a total of 746 single nucleotide polymorphisms (SNPs) that were strongly associated with the chalk phenotype, covered 78 Quantitative Trait Loci (QTL) regions. Among them, 21 were high-value QTLs, as they explained at least 10 % of the phenotypic variance for PGC. A combined epistasis and GWAS was applied to dissect the genetics of the complex chalkiness trait, and its regulatory cascades were validated using gene regulatory networks. Promising novel epistatic interactions were found between the loci of chromosomes 6 (PGC6.1) and 7 (PGC7.8) that contributed to lower PGC. Based on haplotype mining only a few modern rice varieties confounded with a lower chalkiness, and they possess several PGC QTLs. The importance of PGC6.1 was validated through multi-parent advanced generation intercrosses and several low-chalk lines possessing superior haplotypes were identified. The results of this investigation have deciphered the underlying genetic networks that can reduce PGC to 2%, and will thus support future breeding programs to improve the grain quality of elite genetic material with high-yielding potentials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genome‐wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness

Misra

Badoni

Parween

et al. 2020

Plant Biotechnology Journal

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“…Another important grain quality trait is chalkiness, which is generally characterized as an opaque white discoloration in the translucent endosperm caused by the creation of air spaces between irregularly shaped starch granules (Butardo and Sreenivasulu, 2019). An increased proportion of chalky grains lowers the appearance quality of rice, and is also believed to lower the milling quality by increasing the incidence of grain breakage (Del Rosario et al , 1968; Lisle et al , 2000; Wang et al , 2007; Cooper et al , 2008).…”

Section: Introductionmentioning

confidence: 99%

Dissecting the genome-wide genetic variants of milling and appearance quality traits in rice

Misra

Anacleto

Badoni

et al. 2019

Journal of Experimental Botany

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“…Commercial rice ( Oryza sativa ) is available in various grades to meet consumer needs according to price and consumer preferences. The diverse rice germplasm consumed worldwide has high variability in its quality traits and has been linked with the physicochemical properties of the rice grains [ 1 , 2 , 3 , 4 , 5 ]. These traits are related to consumer acceptance of size and shape, color, odor/aroma, purity, homogeneity, and texture [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Aznan

Viejo

Pang

et al. 2021

Sensors

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Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

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Improving Head Rice Yield and Milling Quality: State-of-the-Art and Future Prospects

Cited by 18 publications

References 79 publications

Genome‐wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness

Genome‐wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness

Dissecting the genome-wide genetic variants of milling and appearance quality traits in rice

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

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