## 5 Analysis and Discussion
Soil properties, different types of fertiliser application rate, and yield data from historical yields were used to predict corn yields by [PERSON] et al. [21]. As a result, the palm oil yield prediction research should also take into account a large number of features groups. The efficiency with which palm trees absorb nutrients is directly related to their soil properties. Nutrients like these have a significant impact on palm yield effectiveness [38]. It is possible to estimate palm oil yield by looking at a wide range of soil properties, such as soil electrical conductivity and conductivity to clay and silt, organic carbon content, pH and cation exchange capacity, bulk density, and the percentage of clay, silt, and sand in the soil. The use of remote sensing to examine soil properties in a palm grove could be a promising avenue for future investigation. It is possible to monitor the soil's characteristics from afar because the reflected light has different responses. For example, soil moisture content may be taken into consideration when determining the dielectric characteristics of the spreading wave. It is possible to investigate the relationships between recorded signals and a variety of soil characteristics, including soil texture, soil form, and overall soil structure, in order to develop analytical relationships. However, the estimated parameters, such as soil moisture, LAI, greenness of the palm canopy, and height, that contribute significantly to the yield prediction should be taken into account. A precise yield forecasting model can be created once the connection is defined. Including historical yield data, various climatic data, vegetation data, fertiliser application information, and other types of satellite data could improve yield forecasting performance. Remote sensing makes it possible to quickly gather data on the ground from a large area. For example, it has been used to monitor the environment and topographic conditions [98; 126]. It collects spatial data without any direct contact. According to an increase in demand for oil palm products, numerous studies have been conducted on oil palm plantation mapping using remote sensing. Mapping a palm plantation accurately can have significant economic and environmental advantages. Traditional ML techniques, classical image processing methods, and deep learning methods can all be used to identify the crowns of trees. RF [125; 126] and SVM [111; 116] are two of the most commonly used classifiers for tree crown detection in traditional ML approaches. When compared to traditional image processing methods,ML techniques have made significant progress. Image binarization and segmentation are commonly obtained using high-resolution UAV images such as local maximum filter [111]. However, complex situations like overlapping tree crowns may degrade detection results due to the method's unnecessary labels. Researchers using remote sensing images have increasingly turned to DL-based classifiers, which employ multiscale computational methods, in recent studies to detect trees from satellite images; the most advanced studies use deep learning-based classification [144]. Features can be extracted using DL, which is known for its impressive capacity. With Faster RCNN [156], advances in object detection based on pre-trained models have been made to detect palm trees in the plantation area. It is possible to improve the predictive accuracy of classifiers by optimising their input hyper-parameters, which are highly dependent on their accuracy. In order to improve accuracy, hyperparameter optimisation must be used. Its accuracy was superior to other classifiers, such as SVM, CART, NN, and IGSO-RF classifiers that used the IGSO algorithm to fine-tune the parameters of the traditional RF model [125]. Disease image recognition plays a critical role in the development of innovative agriculture. Oil palm diseases have been studied using a wide variety of diagnostic methods. Categories such as SVM [120], as well as ANN [135] and NB [152], are included.