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Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium

The excess of the chemical fertilizers not only causes the environmental pollution but also has many deteriorating effects including global warming and alteration of soil microbial diversity. In conventional researches, chemical fertilizers and their concentrations are selected based on the knowledg...

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Autores principales: Ramezanpour, Mahmoud Reza, Farajpour, Mostafa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843134/
https://www.ncbi.nlm.nih.gov/pubmed/35157736
http://dx.doi.org/10.1371/journal.pone.0264040
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author Ramezanpour, Mahmoud Reza
Farajpour, Mostafa
author_facet Ramezanpour, Mahmoud Reza
Farajpour, Mostafa
author_sort Ramezanpour, Mahmoud Reza
collection PubMed
description The excess of the chemical fertilizers not only causes the environmental pollution but also has many deteriorating effects including global warming and alteration of soil microbial diversity. In conventional researches, chemical fertilizers and their concentrations are selected based on the knowledge of experts involved in the projects, which this kind of models are usually subjective. Therefore, the present study aimed to introduce the optimal concentrations of three macro elements including nitrogen (0, 100, and 200 g), potassium (0, 100, 200, and 300 g), and magnesium (0, 50, and 100 g) on fruit yield (FY), fruit length (FL), and number of rows per spike (NRPS) of greenhouse banana using analysis of variance (ANOVA) followed by post hoc LSD test and two well-known artificial neural networks (ANNs) including multilayer perceptron (MLP) and generalized regression neural network (GRNN). According to the results of ANOVA, the highest mean value of the FY was obtained with 200 g of N, 300 g of K, and 50 g of Mg. Based on the results of the present study, the both ANNs models had high predictive accuracy (R(2) = 0.66–0.99) in the both training and testing data for the FY, FL, and NRPS. However, the GRNN model had better performance than MLP model for modeling and predicting the three characters of greenhouse banana. Therefore, genetic algorithm (GA) was subjected to the GRNN model in order to find the optimal amounts of N, K, and Mg for achieving the high amounts of the FY, FL, and NRPS. The GRNN-GA hybrid model confirmed that high yield of the plant could be achieved by reducing chemical fertilizers including nitrogen, potassium, and magnesium by 65, 44, and 62%, respectively, in compared to traditional method.
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spelling pubmed-88431342022-02-15 Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium Ramezanpour, Mahmoud Reza Farajpour, Mostafa PLoS One Research Article The excess of the chemical fertilizers not only causes the environmental pollution but also has many deteriorating effects including global warming and alteration of soil microbial diversity. In conventional researches, chemical fertilizers and their concentrations are selected based on the knowledge of experts involved in the projects, which this kind of models are usually subjective. Therefore, the present study aimed to introduce the optimal concentrations of three macro elements including nitrogen (0, 100, and 200 g), potassium (0, 100, 200, and 300 g), and magnesium (0, 50, and 100 g) on fruit yield (FY), fruit length (FL), and number of rows per spike (NRPS) of greenhouse banana using analysis of variance (ANOVA) followed by post hoc LSD test and two well-known artificial neural networks (ANNs) including multilayer perceptron (MLP) and generalized regression neural network (GRNN). According to the results of ANOVA, the highest mean value of the FY was obtained with 200 g of N, 300 g of K, and 50 g of Mg. Based on the results of the present study, the both ANNs models had high predictive accuracy (R(2) = 0.66–0.99) in the both training and testing data for the FY, FL, and NRPS. However, the GRNN model had better performance than MLP model for modeling and predicting the three characters of greenhouse banana. Therefore, genetic algorithm (GA) was subjected to the GRNN model in order to find the optimal amounts of N, K, and Mg for achieving the high amounts of the FY, FL, and NRPS. The GRNN-GA hybrid model confirmed that high yield of the plant could be achieved by reducing chemical fertilizers including nitrogen, potassium, and magnesium by 65, 44, and 62%, respectively, in compared to traditional method. Public Library of Science 2022-02-14 /pmc/articles/PMC8843134/ /pubmed/35157736 http://dx.doi.org/10.1371/journal.pone.0264040 Text en © 2022 Ramezanpour, Farajpour https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ramezanpour, Mahmoud Reza
Farajpour, Mostafa
Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
title Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
title_full Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
title_fullStr Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
title_full_unstemmed Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
title_short Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
title_sort application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843134/
https://www.ncbi.nlm.nih.gov/pubmed/35157736
http://dx.doi.org/10.1371/journal.pone.0264040
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