Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks

ABSTRACT The leaf is a very important plant structure because it allows gas exchange and transformation of light energy into chemical energy. The objective of this research study was to test artificial neural networks (ANNs) to estimate the leaf area (LA) of Ormosia paraensis Ducke and compare their...

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Autores principales:	dos Santos,Ronaldo Oliveira, Neto Soares,Rubiene, Pimentel,Mickelly Paula Queiroz, de Abreu,Jadson Coelho, de Lima,Robson Borges, da Silva e Silva,Breno Marques
Lenguaje:	English
Publicado:	Instituto de Investigaciones Agropecuarias, INIA 2018
Materias:	Adjustment of equations artificial intelligence forest species ImageJ software
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000400511
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spelling	oai:scielo:S0718-583920180004005112018-12-26Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networksdos Santos,Ronaldo OliveiraNeto Soares,RubienePimentel,Mickelly Paula Queirozde Abreu,Jadson Coelhode Lima,Robson Borgesda Silva e Silva,Breno Marques Adjustment of equations artificial intelligence forest species ImageJ software ABSTRACT The leaf is a very important plant structure because it allows gas exchange and transformation of light energy into chemical energy. The objective of this research study was to test artificial neural networks (ANNs) to estimate the leaf area (LA) of Ormosia paraensis Ducke and compare their performance with adjusted statistical models. One hundred forty leaves were selected from seedlings of the species at the leaf age of 100 days after sowing (DAT). The LA was calculated by indirect estimation with ImageJ software, and the linear length (L) and width (W) dimensions were measured with a ruler graduated in centimeters. Afterward, 90 leaves were randomly separated to generate mathematical equations of LA (Y) as a function of linear dimensions (L and W), and 50 leaves to validate the equations. Similarly, 100 networks of the multilayer perceptron (MLP) type were trained with the backpropagation algorithm and the best networks were selected for later validation. The choice of the best equations and ANNs was based on precision and dispersion statistics. The quadratic equation obtained from model (4) demonstrated consistent statistics with the coefficient of determination (R²aj = 87.39) and a low result for the standard error of estimate (SEE = 12.07%). For the ANNs, a correlation coefficient (Ryŷ), varying between 0.9316 and 0.9521, was obtained in the training phase, while it ranged from 0.8522 to 0.8825 in the validation phase, generating lower residues. It is concluded that the ANN performance was higher compared with the conventional regression technique.info:eu-repo/semantics/openAccessInstituto de Investigaciones Agropecuarias, INIAChilean journal of agricultural research v.78 n.4 20182018-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000400511en10.4067/S0718-58392018000400511
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Adjustment of equations artificial intelligence forest species ImageJ software
spellingShingle	Adjustment of equations artificial intelligence forest species ImageJ software dos Santos,Ronaldo Oliveira Neto Soares,Rubiene Pimentel,Mickelly Paula Queiroz de Abreu,Jadson Coelho de Lima,Robson Borges da Silva e Silva,Breno Marques Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks
description	ABSTRACT The leaf is a very important plant structure because it allows gas exchange and transformation of light energy into chemical energy. The objective of this research study was to test artificial neural networks (ANNs) to estimate the leaf area (LA) of Ormosia paraensis Ducke and compare their performance with adjusted statistical models. One hundred forty leaves were selected from seedlings of the species at the leaf age of 100 days after sowing (DAT). The LA was calculated by indirect estimation with ImageJ software, and the linear length (L) and width (W) dimensions were measured with a ruler graduated in centimeters. Afterward, 90 leaves were randomly separated to generate mathematical equations of LA (Y) as a function of linear dimensions (L and W), and 50 leaves to validate the equations. Similarly, 100 networks of the multilayer perceptron (MLP) type were trained with the backpropagation algorithm and the best networks were selected for later validation. The choice of the best equations and ANNs was based on precision and dispersion statistics. The quadratic equation obtained from model (4) demonstrated consistent statistics with the coefficient of determination (R²aj = 87.39) and a low result for the standard error of estimate (SEE = 12.07%). For the ANNs, a correlation coefficient (Ryŷ), varying between 0.9316 and 0.9521, was obtained in the training phase, while it ranged from 0.8522 to 0.8825 in the validation phase, generating lower residues. It is concluded that the ANN performance was higher compared with the conventional regression technique.
author	dos Santos,Ronaldo Oliveira Neto Soares,Rubiene Pimentel,Mickelly Paula Queiroz de Abreu,Jadson Coelho de Lima,Robson Borges da Silva e Silva,Breno Marques
author_facet	dos Santos,Ronaldo Oliveira Neto Soares,Rubiene Pimentel,Mickelly Paula Queiroz de Abreu,Jadson Coelho de Lima,Robson Borges da Silva e Silva,Breno Marques
author_sort	dos Santos,Ronaldo Oliveira
title	Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks
title_short	Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks
title_full	Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks
title_fullStr	Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks
title_full_unstemmed	Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks
title_sort	modeling the leaf area of ormosia paraensis ducke by statistical models and artificial neural networks
publisher	Instituto de Investigaciones Agropecuarias, INIA
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000400511
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_version_	1714205382648266752

Modeling the leaf area of Ormosia paraensis Ducke by statistical models and artificial neural networks

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