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Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and w...

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Autores principales: Bavykina, Maria, Kostina, Nadezhda, Lee, Cheng-Ruei, Schafleitner, Roland, Bishop-von Wettberg, Eric, Nuzhdin, Sergey V., Samsonova, Maria, Gursky, Vitaly, Kozlov, Konstantin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738219/
https://www.ncbi.nlm.nih.gov/pubmed/36501364
http://dx.doi.org/10.3390/plants11233327
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author Bavykina, Maria
Kostina, Nadezhda
Lee, Cheng-Ruei
Schafleitner, Roland
Bishop-von Wettberg, Eric
Nuzhdin, Sergey V.
Samsonova, Maria
Gursky, Vitaly
Kozlov, Konstantin
author_facet Bavykina, Maria
Kostina, Nadezhda
Lee, Cheng-Ruei
Schafleitner, Roland
Bishop-von Wettberg, Eric
Nuzhdin, Sergey V.
Samsonova, Maria
Gursky, Vitaly
Kozlov, Konstantin
author_sort Bavykina, Maria
collection PubMed
description Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.
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spelling pubmed-97382192022-12-11 Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest Bavykina, Maria Kostina, Nadezhda Lee, Cheng-Ruei Schafleitner, Roland Bishop-von Wettberg, Eric Nuzhdin, Sergey V. Samsonova, Maria Gursky, Vitaly Kozlov, Konstantin Plants (Basel) Article Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time. MDPI 2022-12-01 /pmc/articles/PMC9738219/ /pubmed/36501364 http://dx.doi.org/10.3390/plants11233327 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bavykina, Maria
Kostina, Nadezhda
Lee, Cheng-Ruei
Schafleitner, Roland
Bishop-von Wettberg, Eric
Nuzhdin, Sergey V.
Samsonova, Maria
Gursky, Vitaly
Kozlov, Konstantin
Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest
title Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest
title_full Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest
title_fullStr Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest
title_full_unstemmed Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest
title_short Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest
title_sort modeling of flowering time in vigna radiata with artificial image objects, convolutional neural network and random forest
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738219/
https://www.ncbi.nlm.nih.gov/pubmed/36501364
http://dx.doi.org/10.3390/plants11233327
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