Breeding and Genetics of the Oil Palm

Soh, A. C.

doi:10.1016/b978-0-9818936-9-3.50005-8

Cited by 6 publications

(10 citation statements)

References 28 publications

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“…The germplasm of these materials is used to produce hybrid seeds. Vegetative characteristics and the bunch performance of four seedlings introduced to Indonesia in 1848 are consistent [51]. In 1848, Indonesia introduced the vegetative characteristics and consistent performance of four oil palm seedlings [51,52].…”

Section: Breeding Programmesmentioning

confidence: 90%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

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Section: Breeding Programmesmentioning

confidence: 90%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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“…Potential yields of DxP planting materials have increased by an estimated 1.5% per year through breeding with specific male/female parent combinations that show an early track record of performance: this trend in yield increase is expected to continue (Soh, 2004;Corley, 2006). Breeding has particularly improved photosynthetic conversion efficiency (Corley and Lee, 1992) and bunch oil content (Corley and Lee, 1992;Prasetyo et al, 2014;Soh, 2015). Varieties with improved tolerance for cold (Chapman et al, 2003) and drought (Rao et al, 2008) are being further developed.…”

Section: Planting Materialsmentioning

confidence: 99%

On yield gaps and better management practices in Indonesian smallholder oil palm plantations

Woittiez

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“…Currently, studies focus on molecular markers or genotypes for plant breeding purposes but lack research on phenotypes. Breeding studies can be improved by combining phenotypes and genotypes to reduce time, space, and energy in order to produce new cultivars or populations [19]. In the past decade, research has stated that phenotype selection within crosses is not efficient, due to the requirements of the evaluation phase in field trials to identify high-yielding clones.…”

Section: Understanding Oil Palm Breedingmentioning

confidence: 99%

“…Male flowering occurs when the condition of the Dura's palm is pressed. However, Pisifera palms produce female inflorescences in good growing condition but are usually void because Pisifera does not produce oil to the bunch [19]. The importance of oil palm breeding is to improve the quality of palm oil, choose drought tolerance [23], enable the expansion of planting areas, extend the economic lifespan of crops, reduce vertical growth, and maximize oil palm and yields [24].…”

Section: Understanding Oil Palm Breedingmentioning

confidence: 99%

“…Oil Yield Component Oil Quality Traits Low lipase, high stearic and high oleic [19], low free fatty acids content and high iodine value [24], oil quality [13,17,20,23,41,42], quality, and yield [27]. Oil Yield Related Traits Oil weight-to-dry mesocarp (O/DM) [41], total oil yield per palm (O/P), mesocarp-to-fruit weight (M/F), kernel-to-fruit weight (K/F), shell-to-fruit weight (S/F), and fruit-to-bunch (F/B) [19,20,24], oil to bunch [25,42], oil content [42] in mesocarp [12,43] and oil yield [12,14,20,23,44]. Oil Palm Traits FFB [12], Yield [44] [17,20,42] [45], E. Guineensis for traits such as height and high carotene [9,13].…”

Section: Components Types Of Traits Traitsmentioning

confidence: 99%

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

et al. 2021

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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).

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Breeding and Genetics of the Oil Palm

Cited by 6 publications

References 28 publications

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

On yield gaps and better management practices in Indonesian smallholder oil palm plantations

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

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