Application of neural networks to predict volume in eucalyptus
The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models.
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Crop Breeding and Applied Biotechnology
2015
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oai:scielo:S1984-703320150003001252015-09-29Application of neural networks to predict volume in eucalyptusBhering,Leonardo LopesCruz,Cosme DamiãoPeixoto,Leonardo de AzevedoRosado,Antônio MarcosLaviola,Bruno GalveasNascimento,Moysés Genetic parameters gain with selection plant breeding The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models.info:eu-repo/semantics/openAccessCrop Breeding and Applied BiotechnologyCrop Breeding and Applied Biotechnology v.15 n.3 20152015-09-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332015000300125en10.1590/1984-70332015v15n3a23 |
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Bhering,Leonardo Lopes Cruz,Cosme Damião Peixoto,Leonardo de Azevedo Rosado,Antônio Marcos Laviola,Bruno Galveas Nascimento,Moysés |
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Bhering,Leonardo Lopes Cruz,Cosme Damião Peixoto,Leonardo de Azevedo Rosado,Antônio Marcos Laviola,Bruno Galveas Nascimento,Moysés Application of neural networks to predict volume in eucalyptus |
| author_facet |
Bhering,Leonardo Lopes Cruz,Cosme Damião Peixoto,Leonardo de Azevedo Rosado,Antônio Marcos Laviola,Bruno Galveas Nascimento,Moysés |
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Bhering,Leonardo Lopes |
| title |
Application of neural networks to predict volume in eucalyptus |
| title_short |
Application of neural networks to predict volume in eucalyptus |
| title_full |
Application of neural networks to predict volume in eucalyptus |
| title_fullStr |
Application of neural networks to predict volume in eucalyptus |
| title_full_unstemmed |
Application of neural networks to predict volume in eucalyptus |
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application of neural networks to predict volume in eucalyptus |
| description |
The aim of this study was to evaluate the methodology of Artificial Neural Networks (ANN) in order to predict wood volume in eucalyptus and its impacts on the selection of superior families, and to compare artificial neural network with regression models. Data used were obtained in a random block design with 140 half-sib families with five replications at three years of age, and four replications at six years of age, both with five plants per plot. The volume was estimated using ANN and regression models. It was used 2000 and 1500 data to train ANN, and 1500 and 1300 to validate ANN for 3 and 6 years of age, respectively. It is concluded that ANN can help improving the accuracy to measure the volume in eucalyptus trees, and to automate the process of forestry inventory and were more accurate in predicting wood volume than almost all regression models. |
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Crop Breeding and Applied Biotechnology |
| publishDate |
2015 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332015000300125 |
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