## 5 Analysis and Discussion
However, when it comes to the category of factors, there have been very few that have used  
Figure 4: The trend of using categorical factors between 2015 and 2021.  
all of them. Besides, genotype environmental and historical information can be found in general datasets, whereas phenotype data come from the statutory body of the Malaysian Palm Oil Board (MPOB) or Felda Global Ventures (FGV), which have their own procedures. Because of the high cost of capturing data in the field of study, phenotypic data are difficult to communicate. Data storage and analysis tools, phenotype reduction, and other laboratory costs are among the challenges to developing an optimal digital phenotype platform [160]. As a result of these concerns, several gaps must be addressed, such as dealing with a wide range of data sizes and implementing the appropriate data-driven method. When parameter selection is required, techniques for managing and conducting data experiments become complex. In addition, parameter tuning must be performed. More experiments conducted on the data yield superior results. The difficulty in developing techniques for future research is minimising the impact of data inaccuracies within datasets. During testing and training, the first step recommended by this study is to identify and discard low-quality data points [86]. Due to the random selection of features, varying quantities of features deemed too high or too low may result in model-fitting issues and performance fluctuations. According to previous studies, the randomness in the dataset has caused the model to be overfit and forecasting errors to be made [115; 126]. Another possible cause of forecasting failure is a lack of suitable feature selection methods for ML and DL. Another challenge is model selection, which requires data. Accuracy in oil palm breeding models is achieved through a hybrid method in this study. A model selection problem is presented because it transitions from traditional to modern model [147]. Rather than using inefficient and time-consuming traditional modelling methods to optimise crop yields, farmers are now turning to AI models that are more accurate and efficient. Oil palm model selection is an example of how knowledge can be applied in various ways. Evaluating the impact of different parameters on oil palm yields is possible. The artificial intelligence (AI) method is a form of optimisation based on natural selection and inherited principles [159]. Previous studies have employed various classification and regression algorithms to predict palm oil. Figure 6 shows the prediction algorithms based on the previous findings. The extracted data indicate that regression (24 per cent) is the most used palm oil prediction algorithm, followed by clustering, SVM, time-series, and RF. The second most popular algorithms for prediction the clustering approaches, i.e., NJ, UPGMA, and Bayesian network, focusing on the breeding palm oil prediction. Meanwhile, K-means  
Figure 5: Factors category. perceive behaviour on nonlinear variables, though more studies are required to test this claim. The third most preferred algorithm for prediction is SVM, though several flaws must be resolved. The SVM results were more significant than the MSC-PCA hybrid with SVM, SVM-RBF, and SVM-ANN [104; 107]. This result implies that the increased SVM performance is attributed to the improved optimisation techniques for a wide range of parameters [114]. The SVM provides additional kernel functionality, improving the prediction models through feature recognition. This model possesses a more compact management potential than ANN, specifically in its distribution, geometry, and data overflow. Furthermore, the SVM is theorised based on the principle of reducing structural instability, diminishing the high bond error instead of training error [115]. Moreover, this study found various issues in ANN related to DL algorithms. For example, the ANN output involves several hidden layers and types of activation functions.