Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Guimarães,Bruno V. C.
Data de Publicação: 2018
Outros Autores: Donato,Sérgio L. R., Azevedo,Alcinei M., Aspiazú,Ignacio, Silva Junior,Ancilon A. e
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500315
Resumo: ABSTRACT Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible.
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spelling Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networksyield estimationartificial logicproductionOpuntia fícus indicaABSTRACT Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible.Departamento de Engenharia Agrícola - UFCG2018-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500315Revista Brasileira de Engenharia Agrícola e Ambiental v.22 n.5 2018reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)instname:Universidade Federal de Campina Grande (UFCG)instacron:UFCG10.1590/1807-1929/agriambi.v22n5p315-319info:eu-repo/semantics/openAccessGuimarães,Bruno V. C.Donato,Sérgio L. R.Azevedo,Alcinei M.Aspiazú,IgnacioSilva Junior,Ancilon A. eeng2018-06-05T00:00:00Zoai:scielo:S1415-43662018000500315Revistahttp://www.scielo.br/rbeaaPUBhttps://old.scielo.br/oai/scielo-oai.php||agriambi@agriambi.com.br1807-19291415-4366opendoar:2018-06-05T00:00Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)false
dc.title.none.fl_str_mv Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
title Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
spellingShingle Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
Guimarães,Bruno V. C.
yield estimation
artificial logic
production
Opuntia fícus indica
title_short Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
title_full Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
title_fullStr Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
title_full_unstemmed Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
title_sort Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
author Guimarães,Bruno V. C.
author_facet Guimarães,Bruno V. C.
Donato,Sérgio L. R.
Azevedo,Alcinei M.
Aspiazú,Ignacio
Silva Junior,Ancilon A. e
author_role author
author2 Donato,Sérgio L. R.
Azevedo,Alcinei M.
Aspiazú,Ignacio
Silva Junior,Ancilon A. e
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Guimarães,Bruno V. C.
Donato,Sérgio L. R.
Azevedo,Alcinei M.
Aspiazú,Ignacio
Silva Junior,Ancilon A. e
dc.subject.por.fl_str_mv yield estimation
artificial logic
production
Opuntia fícus indica
topic yield estimation
artificial logic
production
Opuntia fícus indica
description ABSTRACT Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible.
publishDate 2018
dc.date.none.fl_str_mv 2018-05-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500315
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dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv 10.1590/1807-1929/agriambi.v22n5p315-319
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
dc.source.none.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental v.22 n.5 2018
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instname_str Universidade Federal de Campina Grande (UFCG)
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reponame_str Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
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