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Artificial neural networks classify cotton genotypes for fiber length. Fiber length is the main trait that needs to be improved in cotton. However, the presence of genotypes x environments interaction for this trait can hinder the recommendation of genotypes with greater... ... Repositório: BDPA Ano de publicação: 2018
Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee. The 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 ara... ... Repositório: BDPA Ano de publicação: 2017
Artificial neural networks to estimate the physical-mechanical properties of amazon second cutting cycle wood. Timber from the second cutting cycle may make up the majority of future crop volumetric. However, there are few studies of the physical and mechanical properties of this timber, which are important to... ... Repositório: BDPA Ano de publicação: 2018
Digital soil mapping using reference area and artificial neural networks. Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artif... ... Repositório: BDPA Ano de publicação: 2016
Artificial neural networks in the classification and identification of soybean cultivars by planting region. Vinte variedades de soja (Glycine max), quatorze convencionais e seis variedades transgênicas (RR) foram analisadas quanto ao teor de proteína, ácido fítico, teor de óleo, fitosteróis, cinzas, minerai... ... Repositório: BDPA Ano de publicação: 2011
Artificial neural networks applied in forest biometrics and modeling: state of the art (January/2007 to July/2018). Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solvin... ... Repositório: BDPA Ano de publicação: 2019
Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks. The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating g... ... Repositório: BDPA Ano de publicação: 2020
Artificial neural networks for adaptability and stability evaluation in alfalfa genotypes. The purpose of this work was to evaluate a methodology of adaptability and phenotypic stability of alfalfa genotypes based on the training of an artificial neural network considering the methodology o... ... Repositório: BDPA Ano de publicação: 2013
Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping. Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromise... ... Repositório: BDPA Ano de publicação: 2013
Selection index as a priori information for using artificial neural networks to classify alfalfa genotypes. The efficiency of a selection index generally depends on the quality of the variance matrixes, which demands controlled experiments. Using Artificial Neural Networks (ANNs) trained from a selection in... ... Repositório: BDPA Ano de publicação: 2019

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