Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee

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 Silva

doi:10.1590/s0100-204x2017000300009

Cited by 11 publications

(10 citation statements)

References 25 publications

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“…For dominant markers in repulsion (allele from the susceptible progenitor Catuai amarelo UFV 2148-57) 1 and -1 were also assigned to the presence and absence of the band, respectively. The codominant markers were coded with 0 for heterozygote, -1 for bands from the resistant progenitor and 1 for bands from the susceptible progenitor (Silva et al, 2017). The genotype data quality control used is described in Pestana et al (2015).…”

Section: Genotyping Of Plantsmentioning

confidence: 99%

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Sousa

Nascimento

Silva

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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Genomic 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 ApparentError 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.

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Section: Genotyping Of Plantsmentioning

confidence: 99%

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Sousa

Nascimento

Silva

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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“…In addition, the use of ANNs in the improvement has already demonstrated the great potential of this methodology in obtained GEBV with simulated studies to classification [30,49]; stability and adaptability [50], and even genomic selection studies [13,17].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, this approach does not require any assumptions about the distribution of phenotypic values as the statistical methods do. ANNs have been used successfully in several breeding studies to predict the genetic merit using simulated [15,16] and real data [17][18][19]. Overall, these studies show that the application of ANN in GS presents great potential for capturing complex interactions since the accuracy values and the bias are, respectively, higher and lower compared with those obtained through traditional GS methodologies (for example, G-BLUP).…”

Section: Introductionmentioning

confidence: 90%

Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

Rosado

Cruz

Barili³

et al. 2020

Agriculture

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Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP.

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“…As Redes Neurais Artificiais (RNA) são capazes de serem utilizadas para predição e classificação de padrões (Silva et al, 2017). O perceptroné uma forma simples de uma rede neural artificial da qual sua principal aplicação se dá nos problemas de classificações de padrões.…”

Section: Introductionunclassified

Predição de pragas e doenças no cafeeiro utilizando Redes Neurais Arti´ficiais

Andrade

Silva

Matos

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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As pragas e doenças no cafeeiro, ocasionadas por bicho-mineiro, broca-do-café, ferrugem e cercosporiose, chegam a atingir ate 50% de uma lavoura cafeeira, podendo causar grandes prejuízos aos cafeicultores. Sendo assim, os sistemas inteligentes são de suma importância para predizer esses danos ao cafeeiro. As redes neurais articiais do tipo perceptron multicamadas, foram os sistemas inteligentes utilizados neste trabalho para prever a porcentagem de ocorrência de pragas e incidência de doenças no cafeeiro. Foram utilizados dados meteorológicos, tais como: temperaturas mínima e máxima, precipitação pluviométrica, umidade relativa do ar, incidência de raios solares e pressão atmosféerica como variáveis de entrada do modelo. O valor dos dados referentes as pragas e doenças se deram quantitativamente e foram coletados no Campo Experimental da EPAMIG de São Sebastião do Paraíso, no sul de Minas Gerais. Foram empregadas as méetricas estatísticas RMSE e R2 para vericar o quão o modelo de rede neuralarticial proposto está predizendo as manifestações de pragas e doenças adequadamente.

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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee

Cited by 11 publications

References 25 publications

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars

Predição de pragas e doenças no cafeeiro utilizando Redes Neurais Arti´ficiais

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