Discrimination of filled and unfilled grains of rice panicles using thermal and RGB images

In recent days, the agricultural research community is focusing on the development of different varieties of aerobic rice, as it consumes less water for its growth. In general, the yield of a crop is considered as a critical performance metric to evaluate different varieties of rice. The count of filled grains in panicles provides a measure for the yield of a crop. The evaluation of yield is a laborious, tedious process and requires human intervention. Hence, this study aims to automate the process for differentiating filled and unfilled grains of rice across different genotypes/varieties and also to help agricultural scientists in the rapid evaluation of different varieties. More precisely, this paper proposes two novel methods that involve RGB and thermal images: (a) Discrimination based on RGB Images (DRI) (b) Discrimination based on Thermal Images (DTI). The study of proposed methods on 15 rice-panicles of different genotypes indicates that DRI method, which involves colour of grains, is found to be challenging to discriminate between filled and unfilled grains. Whereas, DTI method, which makes use of thermal images in differentiating filled and unfilled grains, is found to be profoundly convenient. The performance analysis demonstrates that the proposed DTI method, with averaged absolute errors (AAEs) in discriminating filled grains (2.66%) and unfilled grains (11.389%), outperforms the proposed DRI method with an AAEs in discriminating filled grains (10.664%) and unfilled grains (34.296%). The present investigation resulted in the development of DTI method to discriminate against the filled and unfilled grains across genotypes, and it can be used in rice improvement programs in the future

Efficient genomic selection using ensemble learning and ensemble feature reduction

Genomic selection (GS) is a popular breeding method that uses genome-wide markers to predict plant phenotypes. Empirical studies and simulations have shown that GS can greatly accelerate the breeding cycle, beyond what is possible with traditional quantitative trait locus (QTL) approaches. GS is a regression problem, where one often uses SNPs to predict the phenotypes. Since the SNP data are extremely high-dimensional, of the order of 100 K dimensions, it is difficult to make accurate phenotypic predictions. Moreover, finding the optimal prediction model is computationally very costly. Out of thousands of SNPs, usually only a few influence a particular phenotypic trait. We first of all show how ensemble-based regression techniques give better prediction accuracy compared to traditional regression methods, which have been used in existing papers. We then further improve the prediction accuracy by using an ensemble of feature selection and feature extraction techniques, which also reduces the time to compute the regression model parameters. We predict three traits: grain yield, time to 50% flowering and plant height for which the existing methods give an accuracy of 0.304, 0.627 and 0.341, respectively. Our proposed regression model gives an accuracy of 0.330, 0.674 and 0.458 for these traits. Additionally, we also propose a computationally efficient regression model that reduces the computation time by as much as 90% and gives an accuracy of 0.342, 0.580 and 0.411, respectively

Not Available

Not AvailableThe nutritional quality and food use of groundnut (Arachis hypogaea L.) is mainly governed by oil, fatty acids, protein, and moisture content of kernels. The breeding for higher proportion of oil, protein, and oleic acid in the kernels is an important objective, which needs a non-destructive, rapid, and reliable method for routine estimation in relatively large breeding populations. The present study reports the development of calibration equations in near-infrared reflectance spectroscopy (NIRS) for rapid and non-destructive estimation of kernel quality. Mode of inheritance pattern of a high oleic trait in groundnut was also studied. The best equation for each trait was selected based on the coefficient of determination in calibration and for cross-validation. The current equation gave high fidelity with the reference to biochemical value as indicated by high values of coefficient of determination in external validation (r2 ) for oleic acid (r2 = 0.96), linoleic acid (r 2 = 0.96), moisture (r 2 = 0.96) and moderate for oil (r 2 = 0.89), protein (r 2 = 0.83) and palmitic acid (r 2 = 0.80). The study further developed an efficient NIRS equation to deploy in groundnut breeding. The high oleic trait inheritance pattern was studied in F2:3 population derived from a cross between Spanish bunch normal oleic ICGV 06420 and high oleic SunOleic 95R parents. The results showed duplicate recessive inheritance pattern with a segregation ratio of 15: 1 (normal oleic: high oleic). The outcomes from the inheritance study helps to breed groundnut cultivars for high oleic trait.Not Availabl

Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture

Not Available

Not AvailableBackground: Rice is staple food for more than half of the world’s population including two billion Asians, who obtain 60-70% of their energy intake from rice and its derivatives. To meet the growing demand from human population, rice varieties with higher yield potential and greater yield stability need to be developed. The favourable alleles for yield and yield contributing traits are distributed among two subspecies i.e., indica and japonica of cultivated rice (Oryza sativa L.). Identification of novel favourable alleles in indica/japonica will pave way to marker-assisted mobilization of these alleles in to a genetic background to break genetic barriers to yield. Results: A new plant type (NPT) based mapping population of 310 recombinant inbred lines (RILs) was used to map novel genomic regions and QTL hotspots influencing yield and eleven yield component traits. We identified major quantitative trait loci (QTLs) for days to 50% flowering (R2 = 25%, LOD = 14.3), panicles per plant (R2 = 19%, LOD = 9.74), flag leaf length (R2 = 22%, LOD = 3.05), flag leaf width (R2 = 53%, LOD = 46.5), spikelets per panicle (R2 = 16%, LOD = 13.8), filled grains per panicle (R2 = 22%, LOD = 15.3), percent spikelet sterility (R2 = 18%, LOD = 14.24), thousand grain weight (R2 = 25%, LOD = 12.9) and spikelet setting density (R2 = 23%, LOD = 15) expressing over two or more locations by using composite interval mapping. The phenotypic variation (R2 ) ranged from 8 to 53% for eleven QTLs expressing across all three locations. 19 novel QTLs were contributed by the NPT parent, Pusa1266. 15 QTL hotpots on eight chromosomes were identified for the correlated traits. Six epistatic QTLs effecting five traits at two locations were identified. A marker interval (RM3276-RM5709) on chromosome 4 harboring major QTLs for four traits was identified. Conclusions: The present study reveals that favourable alleles for yield and yield contributing traits were distributed among two subspecies of rice and QTLs were co-localized in different genomic regions. QTL hotspots will be useful for understanding the common genetic control mechanism of the co-localized traits and selection for beneficial allele at these loci will result in a cumulative increase in yield due to the integrative positive effect of various QTLs. The information generated in the present study will be useful to fine map and to identify the genes underlying major robust QTLs and to transfer all favourable QTLs to one genetic background to break genetic barriers to yield for sustained food securityNot Availabl

Efficient genomic selection using ensemble learning and ensemble feature reduction

Genomic selection (GS) is a popular breeding method that uses genome-wide markers to predict plant phenotypes. Empirical studies and simulations have shown that GS can greatly accelerate the breeding cycle, beyond what is possible with traditional quantitative trait locus (QTL) approaches. GS is a regression problem, where one often uses SNPs to predict the phenotypes. Since the SNP data are extremely high-dimensional, of the order of 100 K dimensions, it is difficult to make accurate phenotypic predictions. Moreover, finding the optimal prediction model is computationally very costly. Out of thousands of SNPs, usually only a few influence a particular phenotypic trait. We first of all show how ensemble-based regression techniques give better prediction accuracy compared to traditional regression methods, which have been used in existing papers. We then further improve the prediction accuracy by using an ensemble of feature selection and feature extraction techniques, which also reduces the time to compute the regression model parameters. We predict three traits: grain yield, time to 50% flowering and plant height for which the existing methods give an accuracy of 0.304, 0.627 and 0.341, respectively. Our proposed regression model gives an accuracy of 0.330, 0.674 and 0.458 for these traits. Additionally, we also propose a computationally efficient regression model that reduces the computation time by as much as 90% and gives an accuracy of 0.342, 0.580 and 0.411, respectively