Color classification of corn germplasm using computer vision

A color classification program was developed for classifying the corn germplasm into seven different color groups based on kernel colors. This heuristic based rule supervised color classification program has an overall accuracy of 99%

Background Segmentation and Dimensional Measurement of Corn Germplasm

An automatic thresholding technique was developed to segment the background from the images of corn germplasm (ears of corn). The technique was a modification of Otsu’s algorithm using probability theory. Three different measures were used to evaluate the performance of the modified Otsu’s algorithm for background segmentation and subsequent dimensional measurement of corn germplasm. Modified Otsu’s algorithm was found to perform better than Otsu’s algorithm and was successful in automatic background segmentation of all 80 images of corn germplasm included in the study. This modified algorithm also eliminated the misclassification of exposed cob in the image as background which occurred with Otsu’s algorithm. Subsequent dimensional measurements based on the segmentation by the modified algorithm were also highly accurate

An overview on single nucleotide polymorphism studies in mastitis research

Mastitis is an inflammatory condition of the mammary gland caused by microorganisms as diverse as bacteria, viruses, mycoplasma, yeasts and algae. Mastitis is an economically devastating disease mainly affecting the crossbred cattle in India. Control strategies against mastitis includes antibiotic therapy, vaccination, improvements in dairy cattle husbandry, farm and feeding management etc. but has met with little success.. Mastitis tolerance/susceptibility is difficult to measure directly and hence milk somatic cell count (SCC) or milk somatic cell score (SCS) is used as an indicator trait for mastitis as both traits are highly positively correlated. Single nucleotide polymorphism (SNP) marker is a single base change in a DNA sequence at a given position. SNP markers are the most preferred genetic markers nowadays. Currently most researches worldwide have been targeting molecular high density SNP markers that are linked to mastitis tolerance in an attempt to incorporate to understand the genetics of host resistance to mastitis and this knowledge will be helpful in formulating breeding programmes in an attempt to control mastitis. This article reviews various SNPs which are reported to be significantly associated with mastitis tolerance/susceptibility

Color classification of corn germplasm using computer vision

A color classification program was developed for classifying the corn germplasm into seven different color groups based on kernel colors. This heuristic based rule supervised color classification program has an overall accuracy of 99%.This paper was published in Proc. SPIE 1836, Optics in Agriculture and Forestry, 78 (May 12, 1993), doi:10.1117/12.144046.</p

Genomic selection - Revolutionary breeding practice in Domestic animals

With more and more Single Nucleotide Polymorphisms (SNPs) being identified throughout the genome, some of those SNPs will be found to be located within candidate genes, allowing the researchers to use the candidate gene approach on a genomewide scale. SNPs have some drawbacks but when compared to other markers they are more efficient and SNP consortium is growing to meet the requirements of genome-wide scans. Genomic selection should be able to at least double the rate of genetic gain in the dairy industry but the incorporation of genomic information into the breeding programs must be carefully considered. One needs to have around 2000 genotypes means a large reference population (population with both phenotype and genotype recorded) to achieve meaningful increases in accuracy. The available information, selection objectives, production circumstances and benefit/cost analysis must be evaluated in order to decide whether or not the population is suitable for GS implementation, and which would be the most convenient way, if any, for its implementation. Animal breeders will need to lead the way on the integration of genomic and phenotypic data into a new era of genome-enabled animal improvement and management. [Vet. World 2012; 5(7.000): 433-436

Curbing a Menace

45-46SINCE time immemorial millions and millions of people have been dying due to the menacing activity of a moving bombshell that transmits some of the world’s worst life threatening and devastating diseases. This living jeopardy had once become the nightmare of one of the greatest scientists, Sir Ronald Ross (Nobel prize winner for Physiology in the year 1902). It is none other than the deadly mosquito. Mosquitoes transmit many parasitic and viral diseases like Malaria, Chikungunya, Dengue, Filariasis, Westnile fever etc that are on the rise in many tropical and subtropical countries. Over the past few decades efforts have been made to develop mosquito control strategies by targeting the larval or the adult stages and use of biological agents and genetic engineering techniques against the disease-transmitting mosquitoes

Background Segmentation and Dimensional Measurement of Corn Germplasm

An automatic thresholding technique was developed to segment the background from the images of corn germplasm (ears of corn). The technique was a modification of Otsu’s algorithm using probability theory. Three different measures were used to evaluate the performance of the modified Otsu’s algorithm for background segmentation and subsequent dimensional measurement of corn germplasm. Modified Otsu’s algorithm was found to perform better than Otsu’s algorithm and was successful in automatic background segmentation of all 80 images of corn germplasm included in the study. This modified algorithm also eliminated the misclassification of exposed cob in the image as background which occurred with Otsu’s algorithm. Subsequent dimensional measurements based on the segmentation by the modified algorithm were also highly accurate.This article is from Transactions of the ASAE 38 (1995): 291–297, doi:10.13031/2013.27841. Posted with permission.</p