## 5 Analysis and Discussion
Alternatively, a different optimisation algorithm can be utilised, optimising hidden layers and the ideal activation function, i.e., a genetic algorithm [161; 162]. This substitute procedure may not be the highest performance substitute. The terms \"much used\" and \"best output\" are not similar definitions (See Figure 6). The regression and time-series predictably exceeded the ML and DL techniques as per the anticipated palm oil observations. The parameters and goal parameters could be isolated from regression and time-series [147; 163]. To date, there have been very few investigations into the RF algorithm's potential for classification and regression analysis in agriculture. Palm oil yield estimation may be difficult and unreliable due to varying environmental conditions in different fields. This constraint may be critical in extrapolating the results for RF regression as well as a result of the lack of sufficient training datasets. The developed RF models overestimated the average yield and underestimated the yield below it. As the number of observations and appropriate predictors for training increase, this issue may be mitigated. According to this study, long-term agrometric variables can be predicted using RF regression. It is clear that the ML and DL techniques, as well as LASSO's ability to isolate dynamic correlations between the variables and the target predictor, outperformed LR in palm oil yield prediction [155]. Both clustering and classifier frameworks are used in the selected articles. Since images are used for clustering in certain articles, the article utilises a numerical dataset in conjunction with machine vision rather than ML. The use of clustering architectures may be investigated in depth in order to identify various opportunities regarding this issue. In time  
Figure 6: Widely used palm oil prediction algorithms. series prediction, determining the window size, lag value, and even the features selected as input for the prediction algorithm is standard practice (i.e., LSTM). It is also common for LSTM to use past data to learn from all the features or input it receives [77]. Due to the prevalence of LSTM algorithms in palm oil forecasting, a focus on DL architectures should be investigated. All DL architectures such as ANN and LSTM were found to be the most frequently used in our review articles that used DL approaches. However, LSTM-CART hybrid DL algorithms [140; 144] have also been applied to this problem. In previous crop and palm oil yield prediction research, a wide variety of performance evaluation metrics have been employed. Accuracy is the most utilised metric in the algorithm for predicting crop and palm oil yield. When palm oil prediction models are evaluated using different performance metrics, it is nearly impossible to compare them. A standard and systematic approach, or one single measure, should be used to quantify the model's palm oil prediction accuracy. It is possible to compare crops and palm oil prediction algorithms if the performance metrics used are the same for all of the algorithms. Only [PERSON] et al. [147] used a maximum number of features under the historical yield data, cropland information, and climatic information groups in other previous studies related to palm oil. Palm oil yield has been predicted using only one feature in other studies [30; 56; 110; 140; 151]. Using a large number of features in a crop yield prediction model may be more accurate than using a smaller number of features. Studies, such as [98], show that by combining information from WOFOST crop model outputs data, weather data, crop data, soil data, and yield statistics, they were able to accurately predict soft wheat, spring barley, sunflower, grain maize, sugar beets, and potato yields. Data on water, early-season weed control, late-season weed control, season-long weed control, temperature, and crop management were used to predict soybean yield by [PERSON] et al. [24].