## 5 Analysis and Discussion  
The data-driven projects of various categories in a single dataset are a difficult task; thus, they are frequently overlooked. The issue with these projects includes the differing data complexity and its update frequency; hence, distinct preprocessing methods will be used to clean up the data because of the frequency difference. Various factors must be considered in future research. Table 2 shows how various critical factors, such as environment (E), phenotype (P), and genotype (G), can be divided into different categories. These factors presented evidence of unfinished work based on the literature. As of 2011, researchers have been focusing on these factors, which are used to summarise trends between 2015 and 2021, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author(s)** & **Objective** & **Dataset** & **Feature** & **Methods** & \ \begin{tabular}{c} **Relevant** \\ **Findings/Performances** \\ \end{tabular}  & \begin{tabular}{c} **Factor** \\ **Category** * \\ \end{tabular}  \\ \cline{5  
Numerous factors, including genetics, climate, and disease detection, were found to affect palm oil yield in this study. The term \"phenotypes\" refers to the traits that can be seen and measured in a breeding program, such as increased genetic variation in palm oil yield. Another factor that affected the study's results was the selection of reliable data. Genotype, phenotype, and environmental data from oil palm crops are all difficult to analyse because of the high level of complexity. Most researchers use genotype data to gather breed-specific DNA for selection purposes. Additional data such as bunch characteristics, yield component, and vegetative measurement can be used for relative selection in oil palm. Environmental and historical data are interdependent. Data from both sources can be used to improve the process and save money. Figure 5 shows most of the data features from previous studies utilised to predict palm oil. Seventy-four per cent of studies that rely on the climate and soil and fertilisation (twenty-six per cent) are in the environment category. However, it is impossible to indicate the most effective subfeatures for phenotype factors under each feature group. For instance, there are nine subfeatures listed under \"phenotype factors\" which are utilised extensively, e.g., bunch, fruit, and diseases. Nevertheless, these studies did not specify the optimal set of sub-features under the bunch and yield of palm oil, requiring further research. The ideal phenotype subfeatures based on various regions should also be investigated. Because of the wide range of existing factors, such as diseases, tree detection, and physical characteristics of palm oil yield, predictions in similar fields should be made over several years [45; 105]. Palm oil yield predictions in similar fields should be recorded over several years due to variations in existing factors such as diseases, tree detection, and physical characteristics [45; 105]. Palm oil forecasting does not emphasise phenotype data similarly to genotype representation; therefore, redundant features must be eliminated to ensure the predictive model accuracy. The insufficient data due to the frequency update would hinder the prediction process. Specific data are updated every minute, hour, or day, depending on the volume. However, sporadic data limit the finding of patterns, which occurs when data are collected only once a month. This phenomenon can also occur in the event of substantial missing data and when dealing with small-sized data. Hence, larger-sized datasets are the suggested means of solving this issue. Oil palm phenotype data are challenging to come by due to a lack of available information. Only a few factors reviewed thus far have fully utilised all three, even though this paper has learned that climates under the meteorological factor are critical.