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

Najihah Ahmad Latif, Fatini Nadhirah Mohd Nain, Nurul Hashimah Ahamed Hassain Malim, Rosni Abdullah, Muhammad Farid Abdul Rahim, Mohd Nasruddin Mohamad and Nurul Syafika Mohamad Fauzi
Additional contact information
Najihah Ahmad Latif: School of Computer Sciences, Universiti Sains Malaysia (USM), Gelugor 11800, Pulau Pinang, Malaysia
Fatini Nadhirah Mohd Nain: School of Computer Sciences, Universiti Sains Malaysia (USM), Gelugor 11800, Pulau Pinang, Malaysia
Nurul Hashimah Ahamed Hassain Malim: School of Computer Sciences, Universiti Sains Malaysia (USM), Gelugor 11800, Pulau Pinang, Malaysia
Rosni Abdullah: School of Computer Sciences, Universiti Sains Malaysia (USM), Gelugor 11800, Pulau Pinang, Malaysia
Muhammad Farid Abdul Rahim: FGV Research and Development (R&D) Sdn Bhd, Unit Biak Baka Sawit, Pusat Penyelidikan Pertanian Tun Razak, Jengka 26400, Pahang, Malaysia
Mohd Nasruddin Mohamad: FGV Research and Development (R&D) Sdn Bhd, Unit Biak Baka Sawit, Pusat Penyelidikan Pertanian Tun Razak, Jengka 26400, Pahang, Malaysia
Nurul Syafika Mohamad Fauzi: FGV Research and Development (R&D) Sdn Bhd, Unit Biak Baka Sawit, Pusat Penyelidikan Pertanian Tun Razak, Jengka 26400, Pahang, Malaysia

Sustainability, 2021, vol. 13, issue 22, 1-24

Abstract: 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).

Keywords: oil palm breeding; phenotype; machine learning; framework; sustainable (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/22/12613/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/22/12613/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:22:p:12613-:d:679641

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().