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).

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.

The types of pharmaceutical products include cosmetics and drugs. Some of the pharmaceutical products comprise a mix of drugs and herbs without considering their interaction effects. Drug-herb interactions (DHIs) refer to the interactions between conventional drugs and herb medicines. However, the available information on DHIs is scattered because it has heterogeneous databases and website resources, apart from some of the paid or subscribed databases. Easy access to information on DHIs would allow researchers to explore more. Therefore, this study proposes improvements in the focus web crawler to collect DHIs information from the heterogeneous resources on the Internet, present priority levels of a resource link through anchor text and URLs, and traversing the link with the aid of depth. The improved focused crawler was tested on two algorithms namely the Breadth-First Search (BFS) and PageRank. Information of DHIs crawled 4,744 herbals from the focus web crawler. The accuracy values for Chinese Med Digital Projects and MedlinePlus were 98% for PageRank and 71% for BFS. Additionally, a focused web crawler may gather more relevant web pages in the same amount of time as a wide crawler. Hence, the proposed crawler may successfully gather DHIs on the web in response to the user queries. Povzetek: Razvit je nov algoritem za preiskovanje spleta za iskanje vzorcev medsebojne odvisnosti zdravil.