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Vishnu Varthini Nachimuthu ¹, S. Robin ², D. Sudhakar ³, M. Raveendran ³, S. Rajeswari ², S. Manonmani ²

Affiliations
1 Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu rajisundar93@gmail.com, swamimano@yahoo.co.in, IN
2 Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, IN
3 Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, IN

Abstract

A population panel of 192 rice genotypes comprising traditional landraces and exotic genotypes from 12 countries was evaluated for 12 agro - morphological traits by principal component analysis for determining the pattern of genetic diversity and relationship among individuals. Twelve quantitative characters i.e. plant height, leaf length, number of productive tillers, panicle length, number of filled grains, spikelet fertility, days to 50% flowering; days to harvest maturity, grain length, grain width, grain length width ratio, and single plant yield were measured. The largest variation was observed for number of productive tillers with Coefficient of Variation (CV) of 28.03% followed by number of filled grains per panicle, single plant yield, leaf length , grain length width ratio. Days to maturity has shown the least variation with the CV of 9.74%. Principal component analysis was utilized to examine the variation and to estimate the relative contribution of various traits for total variability. In the current study, Component 1 had the contribution from the traits such as days to 50% flowering, leaf length, plant height, panicle length, days to maturity and number of filled grains which accounted 28.46% of the total variability. Grain width and grain length width ratio has contributed 16.8% of total variability in component 2. The remaining variability of 14.4%, 11.7% and 9.3% was consolidated in component 3, component 4 and component 5 by various traits such as spikelet fertility, single plant yield, grain length and number of productive tillers. The cumulative variance of 80.56% of total variation among 12 characters was explained by the first five axes. Thus the results of principal component analysis used in the study have revealed the high level of genetic variation and the traits contributing for the variation was identified. Hence this population panel can be utilized for trait improvement in breeding programs for the traits contributing for major variation.

Keywords

Genetic Variation, Principal Component Analysis, Rice

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S. Manonmani ¹, Shanta Rangaswamy ¹, K. Dhanush Kumar ¹, Ishan Srivastava ¹, Jonnalagadda Akhilesh ¹, Joshua Issac ¹

Affiliations
1 Rashtreeya Vidyalaya College of Engineering, Bengaluru, Karnataka, IN

Abstract

In today’s medicine field, there is an increasing need to be more efficient and to be able to develop new techniques to diagnose and cure different diseases and ailments. From the wide range of medical images, there has been an initiative to concentrate or restrict the implementation to X-rays, since X-ray images are the only valid analogies that can be compared to vision from a camera with a perspective. Due to the older diagnosis methods implemented, there is an excess of 2D data when compared to 3D data. Therefore, conversion of 2D data to 3D plays an important factor in arriving at the required result with higher efficiency since it is more cost effective to convert 2D images to 3D rather than create a 3D image from scratch. In this work, the first concept used is of defocus method to perform the depth estimation of the given X-ray image, with the help of edge detection and the second concept used is, gradient magnitude calculation along with image matting using Laplacian process to produce a 3D structure of the 2D image.

Keywords

ARE and RMSE, Defocus Method, Image Quality Metrics, Laplacian Matting.

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