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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
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Resultados da Pesquisa
Item Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Sousa, Ithalo Coelho de; Nascimento, Moysés; Silva, Gabi Nunes; Nascimento, Ana Carolina Campana; Cruz, Cosme Damião; Silva, Fabyano Fonseca e; Almeida, Dênia Pires de; Pestana, Kátia Nogueira; Azevedo, Camila Ferreira; Zambolim, Laércio; Caixeta, Eveline TeixeiraGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.Item Trait selection using procrustes analysis for the study of genetic diversity in Conilon coffee(Editora da Universidade Estadual de Maringá - EDUEM, 2020) Pontes, Daiana Salles; Rosado, Renato Domiciano Silva; Cruz, Cosme Damião; Nascimento, Moysés; Oliveira, Ana Maria Cruz; Pensky, Scott MichaelTrait selection is occasionally necessary to save money and time, as well as accelerate breeding program processes. This study aimed to propose two criteria to select traits based on a Procrustes analysis that are poorly explored in genetic breeding: Criterion 1 (backward algorithm) and Criterion 2 (exhaustive algorithm). Then, these two criteria were further compared with Jolliffe’s criterion, which has often been used to select traits in genetic diversity studies. Sixteen agronomic traits were considered, and 40 Conilon coffee (Coffea canephora) accessions were evaluated. This study showed that the flexibility in selecting traits by researcher preference, graphical visualization, and Procrustes statistic through criteria 1 and 2 is a fast and reliable alternative for decision-making. These decisions are based on the removal and addition of traits for phenotyping in studies of Conilon coffee diversity that can be applied to other crops. Other relevant aspects of selection traits criteria were also discussed.Item Effect of the impact of rigid rods on coffee fruit detachment efficiency by mechanical vibrations(Editora UFLA, 2020) Gomes, Ely Queiroz; Santos, Fabio Lúcio; Nascimento, Moysés; Velloso, Nara SilveiraThe mechanization of field activities has been the response of coffee producers to the labor shortage, especially for fruit harvesting. The use of mechanical harvesters allows greater harvesting efficiency and ensures the economic viability of coffee plantations, which currently depend mainly on the reduction in production costs. The principle used for mechanized coffee harvesting is based on the principle of mechanical vibration. The objective of this study was to analyze the dynamic behavior of the coffee fruit-peduncle-branch system under mechanical vibration and the impact of the vibrating rods on the fruit detachment process in association with this behavior. Fragments of coffee branches containing fruits in the unripe and ripe stages were used in this experiment. These samples were subjected to different frequencies (20, 30, 40, and 50 Hz) and amplitudes (0.002, 0.003, and 0.004 m). Another variable analyzed was the form of vibration transmission to the fruits, with the vibrations being transmitted to the samples with or without the impact of the fiberglass rods. The fruit detachment efficiency increased as the ripening stage progressed from unripe to ripe. A higher detachment efficiency occurred with the increase in vibration frequency and amplitude because of the higher vibrational energy imposed on the fruit. The detachment efficiency was low when the vibration was transmitted without the impact of the rods. Conversely, the vibration in combination with impact achieved a mean detachment efficiency of approximately 90%.Item Adaptabilidade e estabilidade via regressão não paramétrica em genótipos de café(Empresa Brasileira de Pesquisa Agropecuária - Embrapa, 2010-01) Nascimento, Moysés; Ferreira, Adésio; Ferrão, Romário Gava; Campana, Ana Carolina Mota; Bhering, Leonardo Lopes; Cruz, Cosme Damião; Ferrão, Maria Amélia Gava; Fonseca, Aymbiré Francisco Almeida daO objetivo deste trabalho foi avaliar uma metodologia de análise de adaptabilidade e estabilidade fenotípica de genótipos de café baseada em regressão não paramétrica. A técnica utilizada difere das demais, pois reduz a infl uência na estimação do parâmetro de adaptabilidade de algum ponto extremo, ocasionado pela presença de genótipos com respostas demasiadamente diferenciadas a determinado ambiente. Foram utilizados dados provenientes de um experimento sobre produtividade média de grãos de 40 genótipos de café (Coffea canephora), com delineamento em blocos ao acaso, com seis repetições. Os genótipos foram avaliados em cinco anos (1996, 1998, 1999, 2000 e 2001), em dois locais (Sooretama e Marilândia, ES) no total de dez ambientes. A metodologia proposta demonstrou ser adequada e efi ciente, pois extingue os efeitos impróprios induzidos pela presença de pontos extremos e evita a recomendação incorreta de genótipos quanto à adaptabilidade.Item Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee(Empresa Brasileira de Pesquisa Agropecuária - Embrapa, 2017-03) Silva, Gabi Nunes; Nascimento, Moysés; Sant’Anna, Isabela de Castro; Cruz, Cosme Damião; Caixeta, Eveline Teixeira; Carneiro, Pedro Crescêncio Souza; Rosado, Renato Domiciano Silva; Pestana, Kátia Nogueira; Almeida, Dênia Pires de; Oliveira, Marciane da SilvaThe objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F 2 population derived from the self-fertilization of the F 1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.