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Tipo:	Artigo de Periódico
Título:	Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks
Autor(es):	Bruno Soares Laurindo Renata Dias Freitas Laurindo Alcinei Mistico Azevedo Fábio Teixiera Delazari José Cola Zanuncio Derly José Henriques da Silva
Resumo:	The efficacy of artificial neural networks (ANN) to solve complex problems can optimize evaluation processes for early blight disease on tomato plants, reducing required time and resources. The objective of the study was to verify the efficiency of ANN to predict the area under the disease progress curve (AUDPC) to reduce the number of assessments and establish the best time to evaluate early blight disease in tomato accessions. The severity of this disease was evaluated in one hundred and thirty-five tomato accessions from the Germplasm Vegetable Bank of the Federal University of Viçosa (BGH-UFV) in three experiments. The area under the disease progress curve (AUDPC) was calculated with data from six evaluations of the disease’s severity. Several ANN MLP types (Multi-Layer-Perceptron) were trained, taking into account AUDPC values for ​ desired output. Different numbers and assessment combinations for early blight disease severity were used as input. ANN’s were efficient at predicting AUDPC and reduced the number of evaluations from six to two. The twelfth and eighteenth days after pathogen inoculation are the best to evaluate the severity of early blight disease. Genotype by environment affects the efficiency in predicting the AUDPC. ANNs were efficient at predicting the area under the early blight disease progress curve (AUDPC) with fewer evaluations, and as such optimized assessment of this disease in tomato accessions.
Assunto:	Tomate Redes neurais (Computação) Inteligencia artificial Tomate - Doenças e pragas Genetica vegetal
Idioma:	eng
País:	Brasil
Editor:	Universidade Federal de Minas Gerais
Sigla da Instituição:	UFMG
Departamento:	ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
Tipo de Acesso:	Acesso Aberto
Identificador DOI:	http://dx.doi.org/10.1016/j.scienta.2017.02.005
URI:	http://hdl.handle.net/1843/40604
Data do documento:	2017
metadata.dc.url.externa:	https://www.sciencedirect.com/science/article/pii/S0304423817300900?msclkid=af1b6270b01d11ec9d191774f906c1b0
metadata.dc.relation.ispartof:	Scientia Horticulturae
Aparece nas coleções:	Artigo de Periódico

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