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

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).

Heritability, genetic advance, and correlation studies of morpho-agronomic traits and brix in Burkina Faso sweet stalk sorghum genotypes

“…It is also well established that progress in crop improvement depends not only on the degree of variability of the desired trait in the source material but also on the level of heritability of the desired trait [21]. The knowledge of heritability helps to determine the breeding methods which might be appropriate for plant improvement [22].…”