Genetic parameters and path analysis of yield and its components in okra at different sowing dates in the Gangetic plains of eastern India

There is continuing need to identify traits that can facilitate selection of productive progenies. For this, 18 genotypes of okra [Abelmoschus esculentus (L.) Moench] were evaluated for the extent of genetic variability, heritability, correlation and path analysis among various morphological, reproductive and nutritional  characters related to fruit yield over two growing seasons in the Gangetic plains of eastern India. Phenotypic co-efficient of variation (PCV) agreed closely with the genotypic co-efficient of variation (GCV) but the magnitude of PCV was higher than GCV for almost all the characters studied during both seasons which reflect the influence of environment on the expression of traits. High PCV and GCV values were shown by fruit yield per plant, numbers of fruit per plant and plant height at flowering during both seasons. The remaining traits  recorded moderate to low PCV and GCV estimates, indicating that selection for these characters will be less  effective. All characters studied exhibited moderate to high heritability. However, pooled genetic advance (GA) expressed as percentage of mean was high for fruit yield per plant, numbers of fruit per plant, plant height at  flowering and fruit weight. Characters showing moderate to high genetic gain also showed high heritability,  indicating that most genetic variations in these characters were due to additive gene effects. From the  correlation and path coefficient analyses, it revealed that the top priority should be given to selection based on numbers of fruit per plant and fruit weight for yield improvement and could be considered while formulating  selection indices in the improvement of okra.Key words: Okra, genetic variability, heritability, correlation, path analysis

Responses of meteorological parameters during August 24, 2016 Myanmar earthquake

5-12A continental plate movement in the Sunda Trench region resulted into the Myanmar Earthquake (M=6.8) of 24 August, 2016. The variations in the lithosphere–atmosphere-ionosphere (LAI) coupling take place during the occurrence of the earthquake. The thermodynamic equilibrium of the troposphere is very much connected with the atmospheric pressure, temperature, humidity, rainfall and wind speed. Some anomalies are observed on the surface temperature and other atmospheric parameters during the period. The anomalous surface latent heat increase takes place within a time interval of several days before a strong earthquake in this earthquake preparation zone. The variations in Lithosphere-Atmosphere-Ionosphere coupling during Myanmar earthquake (M=6.8) resulted in anomalous changes in different meteorological parameters. The variations in LAI may be due to enhanced radon and other greenhouse fluid emanations during the pre and post earthquake period. The meteorological data analyzed to investigate these observations as well as to invoke responses of the Myanmar earthquake. The occurrences of precursors during the pre-and post-periods of this earthquake from 1 August, 2016 to 30 August, 2016 are reported

Responses of meteorological parameters during August 24, 2016 Myanmar earthquake

Myanmar Earthquake (M=6.8) occurred on 24 August 2016 at 10:34 UTC. Anomalies are expected on the surface temperature and other atmospheric parameters. The thermodynamic equilibrium of the troposphere is very much connected with these meteorological parameters. The data of ionospheric variability, air temperature and relative humidity are taken from Chauk Historical Weather, Myanmar ground stations and analysed through origin 5.0 software. The variations of these parameters during the pre-and post-periods of this earthquake from 1 August 2016 to 31 August 2016 are studied in this work. A secondary response before 8 to 9 days and a primary response before 3 to 4 days before the occurrence of this earthquake are obtained. The radioactive Isotopes generate the geothermal energy during decay, distributed between the rock and the natural fluid contained in its fracture regions developed due to the plate movement in the Sunda Trench regions. The stored energy is emitted in the form of heat from earth crust through fractured places and results the anomalous surface latent heat increase before a strong earthquake in the preparation zone. The anomalous surface latent heat causes the variations of air temperature, atmospheric pressure, relative humidity, rainfall and wind speed

Blind Color Image Watermarking Using Fan Beam Transform and QR Decomposition

Digital watermarking has been utilized effectively for copyright protection of multimedia contents. This paper suggests a blind symmetric watermarking algorithm using fan beam transform (FBT) and QR decomposition (QRD) for color images. At first, the original image is transferred from RGB to L*a*b* color model and FBT is applied to b* component. Then the b*component of the original image is split into m × m non-overlapping blocks and QRD is conducted to each block. Watermark data is placed into the selected coefficient of the upper triangular matrix using a new embedding function. Simulation results suggest that the presented algorithm is extremely robust against numerous attacks, and also yields watermarked images with high quality. Furthermore, it represents more excellent performance compared with the recent state-of-the-art algorithms for robustness and imperceptibility. The normalized correlation (NC) of the proposed algorithm varies from 0.8252 to 1, the peak signal-to-noise ratio (PSNR) varies from 54.1854 to 54.1892, and structural similarity (SSIM) varies from 0.9285 to 0.9696, respectively. In contrast, the NC of the recent state-of-the-art algorithms varies from 0.5193 to 1, PSNR varies from 38.5471 to 52.64, and SSIM varies from 0.9311 to 0.9663, respectively

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Not AvailableThe yellow vein mosaic virus (YVMV) is one of the most serious diseases in okra production, often causing severe losses in commercial fields. Identifying and deploying resistant genotypes and understanding the inheritance of YVMV disease resistance are essential for the okra geneticists to develop an effective breeding strategy. Genetic control of the host resistance to YVMV disease of okra was studied employing six generations (P1, P2, F1, F2, BC1, BC2) of three selected crosses: Tolerant × Tolerant (T × T), Tolerant × Susceptible (T × S) and Susceptible × Susceptible (S × S) among two tolerant and susceptible genotypes. Relationship between disease reaction and different biochemical parameters of the parents and hybrids at three phenological stages (Pre-flowering, flowering and post-flowering) was studied. The inheritance study amply indicated that tolerance to YVMV disease was conditioned by two duplicate dominant genes in Tolerant × Tolerant cross, and by two complementary dominant genes in Tolerant × Susceptible cross. The significant scaling tests and joint scaling test also indicated the presence of digenic epistasis for both the disease reaction characters. The study also suggested that tolerant genotypes appeared in the progeny of even Tolerant × Susceptible cross. Some of the enzyme activities (peroxidase and polyphenol oxidase) and proximate compositions (total phenol and ascorbic acid) in okra leaf exhibited consistent and significant negative correlation with PDI of YVMV disease even over the growth stages suggesting their implication as selection indices for identification of genotype tolerant to YVMV disease. The results suggested modified bulk method of breeding through deferred selection after attaining homozygosity for maximum heterozygous loci.Not Availabl

Road Detection and Segmentation using OpenCV

In today’s world, road detection and segmentation plays an important role in navigation system and generally used to detect new roads and patterns in the region/area. Its main aim is to provide navigation to the autonomous vehicle and robot on the ground. With the help of road detection and segmentation, it will be useful for us in finding correct road path where the vehicle can move as supportive vehicles thus preventing any collision with an obstacles on road. In this paper, a new technique for road detection and segmentation is proposed by using OpenCV(Open Source Computer Vision Library) .OpenCV is basically a library functions which aimed to provide a real-time computer vision. It consist of various methods through which an autonomous vehicle can detect obstacles on road and navigate it accordingly. It have higher accuracy with an average of 90-95%