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Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield
BACKGROUND: Studying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273667/ https://www.ncbi.nlm.nih.gov/pubmed/37328913 http://dx.doi.org/10.1186/s13007-023-01035-9 |
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author | Shahsavari, Masoud Mohammadi, Valiollah Alizadeh, Bahram Alizadeh, Houshang |
author_facet | Shahsavari, Masoud Mohammadi, Valiollah Alizadeh, Bahram Alizadeh, Houshang |
author_sort | Shahsavari, Masoud |
collection | PubMed |
description | BACKGROUND: Studying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY. RESULTS: To achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019–2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R(2)) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R(2) = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R(2) = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY. CONCLUSION: The results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-01035-9. |
format | Online Article Text |
id | pubmed-10273667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102736672023-06-17 Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield Shahsavari, Masoud Mohammadi, Valiollah Alizadeh, Bahram Alizadeh, Houshang Plant Methods Research BACKGROUND: Studying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY. RESULTS: To achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019–2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R(2)) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R(2) = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R(2) = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY. CONCLUSION: The results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-01035-9. BioMed Central 2023-06-16 /pmc/articles/PMC10273667/ /pubmed/37328913 http://dx.doi.org/10.1186/s13007-023-01035-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Shahsavari, Masoud Mohammadi, Valiollah Alizadeh, Bahram Alizadeh, Houshang Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield |
title | Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield |
title_full | Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield |
title_fullStr | Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield |
title_full_unstemmed | Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield |
title_short | Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield |
title_sort | application of machine learning algorithms and feature selection in rapeseed (brassica napus l.) breeding for seed yield |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273667/ https://www.ncbi.nlm.nih.gov/pubmed/37328913 http://dx.doi.org/10.1186/s13007-023-01035-9 |
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