Model-assisted phenotyping by digital images in papaya breeding program

Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the THB variety and UENF/Caliman-01 hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In THB fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.

Model-assisted phenotyping by digital images in papaya breeding program

Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the THB variety and UENF/Caliman-01 hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In THB fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.(AU)

Understanding the uterine environment in early pregnancy in cattle: How have the omicsenhanced our knowledge?

Early pregnancy loss in cattle can be attributed to a myriad of sources. One key factor that can influence early pregnancy success or loss is the influence and interactions between the maternal environment and the developing embryo/conceptus. Recent advancesin high-throughput ‘omics' technologies coupled with improved bioinformatics capabilities represent a promising avenue for enhancing our understanding of fundamental developmental events– which would have direct agricultural, veterinary, and economic benefits. Thusly this review revolves around recent applications of advanced transcriptomic, proteomic, and metabolomic analyses within a bovine uterine secretomic and interactomic context, with an overriding aim to highlight the advantages of these emerging fields whilst identify ingareas for improvement, consideration, and further research and development.

Understanding the uterine environment in early pregnancy in cattle: How have the omicsenhanced our knowledge?

Early pregnancy loss in cattle can be attributed to a myriad of sources. One key factor that can influence early pregnancy success or loss is the influence and interactions between the maternal environment and the developing embryo/conceptus. Recent advancesin high-throughput ‘omics' technologies coupled with improved bioinformatics capabilities represent a promising avenue for enhancing our understanding of fundamental developmental eventswhich would have direct agricultural, veterinary, and economic benefits. Thusly this review revolves around recent applications of advanced transcriptomic, proteomic, and metabolomic analyses within a bovine uterine secretomic and interactomic context, with an overriding aim to highlight the advantages of these emerging fields whilst identify ingareas for improvement, consideration, and further research and development.(AU)

ANÁLISE DE IMAGENS E BIOMETRIA NA IDENTIFICAÇÃO DE MATERNIDADE DE OVOS DE CODORNA

Codornas são animais modelo para diversas áreas das ciências da vida, bem como uma importante espécie para produção de carne e ovos ao redor do mundo. A produção de ovos, seja como alimento ou como meio reprodutivo, frequentemente se dá em gaiolas coletivas, dificultando a identificação individual para controle da produção e em programas de melhoramento genético. O objetivo do presente trabalho foi testar algoritmos de aprendizado estatístico e esquemas de alojamento de codornas que otimizem a identificação da produção, baseada em características externas de seus ovos. Foram utilizados dados de 90 aves, com no mínimo dez ovos cada, sendo testados quatro algoritmos de aprendizado estatístico usando validação cruzada, além de verificar a influência do número de codornas por gaiola e métodos para designar aves a cada gaiola. Os modelos de melhor desempenho consistem no uso de dez variáveis do ovo: peso, largura, altura, proporção de área da casca com padrões, intensidade de vermelho, de azul e de verde, matiz, saturação e luminosidade da cor de fundo dos ovos. A acurácia da classificação é aumentada em gaiolas com menor número de codornas (máxima com três aves) e com direcionamento para aumento da variância dentro de gaiola. O método apresentado mostra viabilidade para uso prático e tem possibilidade de melhoria pelo uso futuro de novas variáveis e métodos mais avançados.

Quail are animal models for many fields of life sciences, as well as an important species for meat and egg production worldwide. Egg production, both as food or as for breeding purposes is often based on multiple-hen cages, hindering individual identification for control of production and in-breeding programs. The aim of the present study was to test algorithms of statistical learning and housing schemes for quail that optimize individual laying control based on quail egg external features. 90 birds were used, with a minimum of ten eggs each, four statistical learning algorithms with cross-validation were tested, as well as verifying the influence of number of quail per cage and methods to assign the birds to each cage. Model with better performance consist in the use of ten variables per egg: weight, height, width, eggshell ratio of patterned area, hue, saturation, lightness, intensity of red, green, and blue of egg background color. The classification accuracy increases when cages have less quail (maximum of three birds) and aimed to increase inside-cage variance. The present method shows feasibility for real-world data and with possibility of improvements with new features and more advanced methods.