## 5 Analysis and Discussion
In this regard, traditional ML methods have some drawbacks when it comes to monitoring palm oil disease. Furthermore, existing methods are heavily reliant on disease images that are of high quality. As a result of these methods, which include image preprocessing, image segmentation, feature extraction, and classification, as well as significant operations that add complexity and delay implementation, the implementation can be significantly delayed. Training is challenging using traditional ML methods, especially when the training dataset is large. Deep learning and transfer learning, two of the most recent advanced ML techniques, have the potential to aid in the development of oil palm disease recognition. Oil palm plantations require a lot of monitoring, which takes time and effort.RF [118], ANN [137], and hybrid CNN-SVM [110] are just a few of the ML approaches that have been used to track palm growth via remote sensing in the past. Nevertheless, landowners must extract useful information from remote sensing data. There is a chance that this will lead to solutions via deep learning, transfer learning, and recognition of objects. Palm oil yield prediction is only indirectly addressed in a few ML-based studies conducted in the industry. So far, there have been five studies examining how well scientists can predict phenotype palm oil yields [56; 87; 141; 150; 151]. In light of these studies, it is difficult to determine which algorithm is best for predicting palm oil yields. It is possible to predict palm oil yield with the help of some regression algorithms based on ML (ML). Multiple algorithms should be considered instead of a single algorithm to improve prediction model robustness. It is a common practice in the palm oil industry to employ SVM models and algorithms. Perhaps the most critical part of this success is regularisation. Outliers and noise are common in agricultural data, making it challenging to analyse. The classifier's generalisation capabilities may be improved through regularisation, which could help solve this issue [114]. The regularised linear SVM classifier outperformed the RBF classifier in this study. For example, the regularisation parameter C and the RBF width using kernel 2 require manual adjustment in SVM. These benefits occurred as a result of the low execution speed. In addition, because the SVM decision rule is a simple linear function in the kernel space, it is stable and has low variance [114]. It is critical to obtain low variability in agricultural data because features are highly dynamic and change over time. SVM's ability to withstand the curse of dimensionality may be the final possible explanation for the problem. Small training sets and high-dimensional feature vectors have allowed SVM to achieve excellent results [114]. In terms of finding a nonlinear pattern in data, the key advantages of RF are its generalisation performance, high speed, and the use of an ensemble of tree-structured classifiers. It has low data preprocessing requirements in training because it is robust regarding to unit differences and can make accurate predictions on sparsely annotated data. The RF algorithm yields better results in the selection of features. Meanwhile, the input hyperparameters play a vital role in RF. High-dimensional feature vectors can benefit from SVM, one of the best classification algorithms. To solve the problem of high dimensionality, dynamic classifiers can also consider a sequence of feature vectors rather than just one high-dimensional feature vector. Another benefit of using these methods is that they can deal with large datasets. A combination of classifiers may be used to solve this problem because it reduces the variance, especially when there is no stationarity in the dataset. This method is still useful, but it may be outperformed by a classifier that uses a combination of RBF and SVM. Due to the limited number of training datasets, simpler techniques such as LDA may be used. The k-NN algorithms are rarely employed in the palm oil industry due to the curse of dimension.