Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
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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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 |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500315 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000500315 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1807-1929/agriambi.v22n5p315-319 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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 reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online) instname:Universidade Federal de Campina Grande (UFCG) instacron:UFCG |
instname_str |
Universidade Federal de Campina Grande (UFCG) |
instacron_str |
UFCG |
institution |
UFCG |
reponame_str |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
collection |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG) |
repository.mail.fl_str_mv |
||agriambi@agriambi.com.br |
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1750297686020456448 |