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

Genetic Dissection of Epistatic Interactions Contributing Yield-Related Agronomic Traits in Rice Using the Compressed Mixed Model

Rice (Oryza sativa) is one of the most important cereal crops in the world, and yield-related agronomic traits, including plant height (PH), panicle length (PL), and protein content (PC), are prerequisites for attaining the desired yield and quality in breeding programs. Meanwhile, the main effects and epistatic effects of quantitative trait nucleotides (QTNs) are all important genetic components for yield-related quantitative traits. In this study, we conducted genome-wide association studies (GWAS) for 413 rice germplasm resources, with 36,901 single nucleotide polymorphisms (SNPs), to identify QTNs, QTN-by-QTN interaction (QQI), and their candidate genes, using a multi-locus compressed variance component mixed model, 3VmrMLM. As a result, two significant QTNs and 56 paired QQIs were detected, amongst 5219 genes of these QTNs, and 26 genes were identified as the yield-related confirmed genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH, and LOG and QsBZR1 for PL. To reveal the substantial contributions related to the variation of yield-related agronomic traits in rice, we further implemented an enrichment analysis and expression analysis. As the results showed, 114 genes, nearly all significant QQIs, were involved in 37 GO terms; for example, the macromolecule metabolic process (GO:0043170), intracellular part (GO:0044424), and binding (GO:0005488). It was revealed that most of the QQIs and the candidate genes were significantly involved in the biological process, molecular function, and cellular component of the target traits. The demonstrated genetic interactions play a critical role in yield-related agronomic traits of rice, and such epistatic interactions contributed to large portions of the missing heritability in GWAS. These results help us to understand the genetic basis underlying the inheritance of the three yield-related agronomic traits and provide implications for rice improvement.

Verifying the Breeding Value of A Rare Haplotype of Chalk7, GS3, and Chalk5 to Improve Grain Appearance Quality in Rice

Grain quality is a key determinant of commercial value in rice. Efficiently improving grain quality, without compromising grain yield, is a challenge in rice breeding programs. Here we report on the identification and application of a grain quality gene, Chalk7, which causes a slender shape and decreases grain chalkiness in rice. Three allele-specific markers for Chalk7, and two other grain genes (GS3 and Chalk5) were developed, and used to stack the desirable alleles at these loci. The effects of individual or combined alleles at the loci were evaluated using a set of near-isogenic lines, each containing one to three favorable alleles in a common background of an elite variety. We found that the favorable allele combination of the three loci, which rarely occurs in natural rice germplasm, greatly reduces chalky grains without negatively impacting on grain yield. The data for newly developed allele-specific markers and pre-breeding lines will facilitate the improvement of grain appearance quality in rice.

Paddy seed variety identification using T20-HOG and Haralick textural features

The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.

Enhancing the functional properties of rice starch through biopolymer blending for industrial applications: A review

Rice starch has been used in various agri-food products due to its hypoallergenic properties. However, rice starch has poor solubility, lower resistant starch content with reduced retrogradation and poor functional properties. Hence, its industrial applications are rather limited. The lack of comprehensive information and a holistic understanding of the interaction between rice starch and endo/exogenous constituents to improve physico-chemical properties is a prerequisite in designing industrial products with enhanced functional attributes. In this comprehensive review, we highlight the potentials of physically mixing of biopolymers in upgrading the functional characteristics of rice starch as a raw material for industrial applications. Specifically, this review tackles rice starch modifications by adding natural/synthetic polymers and plasticizers, leading to functional blends or composites in developing sustainable packaging materials, pharma- and nutraceutical products. Moreover, a brief discussion on rice starch chemical and genetic modifications to alter starch quality for the deployment of rice starch industrial application is also highlighted.

Systems-based rice improvement approaches for sustainable food and nutritional security

An integrated research approach to ensure sustainable rice yield increase of a crop grown by 25% of the world's farmers in 10% of cropland is essential for global food security. Rice, being a global staple crop, feeds about 56% of the world population and sustains 40% of the world's poor. At ~ $200 billion, it also accounts for 13% of the annual crop value. With hunger and malnutrition rampant among the poor, rice research for development is unique in global food and nutrition security. A systems-based, sustainable increase in rice quantity and quality is imperative for environmental and biodiversity benefits. Upstream 'discovery' through biotechnology, midstream 'development' through breeding and agronomy, downstream 'dissemination and deployment' must be 'demand-driven' for 'distinct socio-economic transformational impacts'. Local agro-ecology and livelihood nexus must drive the research agenda for targeted benefits. This necessitates sustained long-term investments by government, non-government and private sectors to secure the future food, nutrition, environment, prosperity and equity status.

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

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.

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

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.

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

Higher head rice yield (HRY), which represents the proportion of intact grains that survive milling, and lower grain chalkiness (opacity) are key quality traits. We investigated the genetic basis of HRY and chalkiness in 320 diverse resequenced accessions of indica rice with integrated single- and multi-locus genome-wide association studies using 2.26 million single-nucleotide polymorphisms. We identified novel haplotypes that underly higher HRY on chromosomes 3, 6, 8, and 11, and that lower grain chalkiness in a fine-mapped region on chromosome 5. Whole-genome sequencing of 92 IRRI breeding lines was performed to identify the genetic variants of HRY and chalkiness. Rare and novel haplotypes were found for lowering chalkiness, but missing alleles hindered progress towards enhancing HRY in breeding material. The novel haplotypes that we identified have potential use in breeding programs aimed at improving these important traits in the rice crop.