## 5 Analysis and Discussion
However, k-NN may be superior if only low-dimensional feature vectors are taken into account in agriculture and the palm oil industry, or if no feature vectors are needed. In remote sensing, the ANN is widely used to predict vegetation parameters and crop yields because it can retrieve complex, dynamic, and non-linear patterns from the data. Many libraries and software tools are readily available, making them the most basic form of ML. A few drawbacks exist in practical applications, such as the rate at which neurons are learned, how many neurons are chosen for the hidden layers, and the overfitting issue when using a large training dataset. Large datasets cause the process to slow down. As the number of epochs required increases, backpropagation networks become slower in training. Using a CNN for image data with a large training set is a good option. A leaf image can identify a disease, map an oil palm plantation using remote sensing-based spectral images, and so on. The use of advanced CNN architectures, such as Faster R-CNN, is also highly effective for object recognition-based tasks. It is possible to predict palm oil yield and oil palm price using regression-based algorithms such as RF, ANN, and SVR. In addition, to improve the predictability of the model, an ensemble of multiple algorithms should be investigated rather than a single algorithm. In DL, even though LSTM appears to be the best method in general, LSTM has primarily been applied to data pertaining to palm oil age detection, environment, as well as price prediction. Palm oil yield requires additional research. It is also worth noting that there are several possible reasons for the LSTM's fluctuating performance, including the number of indicators used and the method's limitation. According to [59; 77], while the sequence-to-sequence architecture of LSTM can store long-term memories, its internal representation is limited to a fixed length. Internal representations or input sequences of LSTMs have a fixed length because information is divided into small pieces for easier recall. Various random weight initialisations impact LSTMs, and as a result, they behave in a manner similar to that of a feed-forward neural network. As a result, many unresolved issues must be addressed to improve or resolve the issue at hand. LSTM feature engineering and a basic grasp of the phenotypic factors of palm oil data can enhance performance in this area. The current performance of LSTM is expected to improve with additional parameter tuning and additional learning methods. The DL is a subset of ML, commonly believed to be significantly persuasive for palm oil prediction. However, the only difference between ML and DL is that the latter is inefficient for a limited training dataset; thus, fundamental components are automatically removed from this dataset. The attributes of other samples must be manually removed to preclude the use of successful DL. Hence, detailed studies must be conducted on the use of DL strategies in palm oil, and there is still room to delve into the performance of algorithms. The research has shown that DL usage in forecast time-series is superior to basic ML in average performance; hence, future studies can focus on this idea. ML data-driven models were unable to extract features from multifactor timing data efficiently. According to this study, most models did not reflect the data's phenotypic characteristics. Time-series has shown to outperform other forecasting methods with minor forecasting errors.