## 7 Conclusions  
This study examined existing research on ML in palm oil conducted over the last decade, categorised into unsupervised, supervised, and deep learning analyses. The critical objective of this study aimed to identify the breeding prediction of oil palms derived from the clustering category. Furthermore, the study incorporated regression analyses, including determining FFB yield, tree detection, and climate. The findings from the research were primarily focused on tree detection, the observation of palm oil plantation areas, and identifying the ripeness of fruit bunches. However, models for forecasting disease were not widely used, i.e., in fruit yield, oil production, and breeding.  
Notably, SVM, RF, LR, and SVR are the most promising conventional machine learning architectures. Additionally, DL models such as LSTM and ANN are used to estimate crop yields in the field. This selection has been made based on most of the algorithms used in previous studies, hybrid modelling, comparison, and performance evaluation. Some feature selection algorithms and performing ensemble methods identify and treat missing and outliers' values, computational complexity, and model fit, as well as outperforming models, and should be examined comprehensively in order to determine the best model. Research on palm oil yield prediction is scarce, according to the findings of the current review. In addition, very few feature sets and ensemble methods have been used in the existing palm oil yield prediction studies, resulting in large discrepancies between the predicted and actual palm oil yield. When it comes to palm oil yield predictions, it is too early to speculate on the best set of features, models to use, and the appropriate ensemble models. As a result, more studies involving a wide range of features and prediction algorithms are needed. Extensive research on crop yield prediction and palm oil yield prediction is expected to be based on this paper.  
Nevertheless, most of the data used in palm oil research were climatic from various sources. For example, identifying suitable land requires various factors that must be considered, e.g., soil classification, automated pest and weed detection, and optimising fertiliser use. This list of factors can be extended to identifying the symptoms of sunlight and water limitations in palm oil crops and seed assessment.  
The findings from this review highlight current palm oil and ML research from various angles. Innovative ML practices, such as big data analytics and automated information extraction, support the advancement of knowledge-based palm oil. However, research trends indicate that existing MLAs and techniques were not adequately coupled to support effective decision-making systems compared to other ML application domains. Thus, the current research is insufficient to design practical tools that increase yields, quality, and plantation sustainability. A vivid idea could be presented on how ML can enhance palm oil development and inspire researchers to find relevant solutions in this area. Overall, this article may support the palm oil industry's automation and intelligence development. However, future work should include a technical review of other ML models used in palm oil agriculture.  
Conceptualization, F.N.M.N. and N.H.A.H.M.; writing--original draft preparation, F.N.M.N.; visualization, F.N.M.N.; writing--review and editing, F.N.M.N., N.H.A.H.M., R.A., M.A.A.M. and N.S.M.F.; supervision, project administration, and funding acquisition, N.H.A.H.M. and R.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded via the Long-term Research Grant (LRGS) by Malaysian Research University Network (MRUN)--203.PKOMP.6777002.  
Not applicable.  
We are grateful for the support provided by Universiti Sains Malaysia, our collaborator FGV Research and Development (R&D) Sdn Bhd, and anyone significant.