Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptu...

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Autores principales:	Najihah Ahmad Latif, Fatini Nadhirah Mohd Nain, Nurul Hashimah Ahamed Hassain Malim, Rosni Abdullah, Muhammad Farid Abdul Rahim, Mohd Nasruddin Mohamad, Nurul Syafika Mohamad Fauzi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	oil palm breeding phenotype machine learning framework sustainable Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/48716f28681e4606a6dcf9146fada1d2
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spelling	oai:doaj.org-article:48716f28681e4606a6dcf9146fada1d22021-11-25T19:02:34ZPredicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning10.3390/su1322126132071-1050https://doaj.org/article/48716f28681e4606a6dcf9146fada1d22021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12613https://doaj.org/toc/2071-1050Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).Najihah Ahmad LatifFatini Nadhirah Mohd NainNurul Hashimah Ahamed Hassain MalimRosni AbdullahMuhammad Farid Abdul RahimMohd Nasruddin MohamadNurul Syafika Mohamad FauziMDPI AGarticleoil palm breedingphenotypemachine learningframeworksustainableEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12613, p 12613 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	oil palm breeding phenotype machine learning framework sustainable Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	oil palm breeding phenotype machine learning framework sustainable Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Najihah Ahmad Latif Fatini Nadhirah Mohd Nain Nurul Hashimah Ahamed Hassain Malim Rosni Abdullah Muhammad Farid Abdul Rahim Mohd Nasruddin Mohamad Nurul Syafika Mohamad Fauzi Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning
description	Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).
format	article
author	Najihah Ahmad Latif Fatini Nadhirah Mohd Nain Nurul Hashimah Ahamed Hassain Malim Rosni Abdullah Muhammad Farid Abdul Rahim Mohd Nasruddin Mohamad Nurul Syafika Mohamad Fauzi
author_facet	Najihah Ahmad Latif Fatini Nadhirah Mohd Nain Nurul Hashimah Ahamed Hassain Malim Rosni Abdullah Muhammad Farid Abdul Rahim Mohd Nasruddin Mohamad Nurul Syafika Mohamad Fauzi
author_sort	Najihah Ahmad Latif
title	Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning
title_short	Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning
title_full	Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning
title_fullStr	Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning
title_full_unstemmed	Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning
title_sort	predicting heritability of oil palm breeding using phenotypic traits and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/48716f28681e4606a6dcf9146fada1d2
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Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

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