Oil palm trees (Elaeis guineensis) is an important species for bio-energy agribusiness. Despite the rapid growth of oil palm tree plantations mostly in tropical countries to support global demand for biofuels, problems such as diseases can reduce the productivity and survival rate of palm trees which have adverse impact to the business. Therefore, palm plantation needs regular tree counting for inventory and health monitoring. Thanks to the rapid development of remote sensing technology deep learning-based computer vision, these two intertwined technologies help to automate tree counting. Continuous improvement in this domain is expected to improve classification. In this study, YOLOv5 model was implementedfor tree counting using the palm aerial imagery dataset from Papua, Indonesia. UAV images were used to classification of trees into five distinct classes, namely healthy, smallish, yellowish, mismanaged, and dead palms. We achieved average F1-score of 0.895 for 5 classes, which outperformed Faster R-CNN (0.706) and CNN ResNet-101 (0.493). The strength of our YOLOv5 model is high precision for all 5 classes above 0.961. In the effort to further optimise YOLOv5, further improvements can be achieved by optimising the parameters. This was achieved using Genetic Algorithm to optimise the parameters. The final average F1-score of this model on the five palm classes achieves 0.915. This application provides fast, robust, and accurate oil palm tree counting that can be applied elsewhere in the world.