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).

FGV is one of the largest plantation companies and crude palm oil producer in the world. Experienced in oil palm breeding research for fifty years since 1960s mainly to improve oil palm yield in term of FFB production and OER. Managing breeding research data is a challenges in any plant breeding research. As technology advancement for the past ten years witnessing the introducing GIS technology, remote sensing, precision agriculture and IoT, information technology plays main role to manage the information in form of application and relational database system to store and linking between information for the long years of research. Over the years, data collected will increase in number and become more difficult to manage. Therefore implementation of technology was used using database management system in order to tackle this problem and has been called FGV Integrated Breeding System (FIBS). The objective is to store collection of data and information of FGV oil palm breeding research such as breeding background, project information, crossing operation and field data. The database was developed using combination of powerful open source web development platform which are Linux as the operating system, Apache as the web server, MySQL as the relational database management system and PHP as the object oriented scripting language. Implementation of information system helps improve data integrity & traceability, reduce data redundancy and provide easy access to information for researcher. In conclusion, as technology rapid growth through the years ahead, towards the precision agriculture and advancement in breeding through biotechnology, advancement and improvement must be made concurrently so no technology are left behind.

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.