Palm Oil Tree Canopy Identification Using Deep Learning Approach (Case Study: Tanjung Gusta District, North Sumatera)

Abstract The palm oil plantation industry in Indonesia has growing rapidly as demand for palm oil increases globally. This needs to be supported by technological innovation to increase palm oil production. One of them is to integrate the power of artificial intelligence technology. This research aims to develop a robust and accurate method for segmenting oil palm trees in plantation areas. Leveraging deep learning algorithms techniques, the research explores the potential of SAM in accurately delineating individual oil palm trees derived from aerial imagery data. The study also involves the development of a comprehensive and versatile labelled dataset to support the training and validation of the deep learning models for oil palm tree counting and segmentation. The performance of the proposed approach is evaluated and discussed critically. This research demonstrates the potential of deep learning algorithms for large-scale mapping and accurate counting of oil palm trees in plantation areas. The author hopes that the result and analysis of this research will give insight and improvement in detecting oil palm trees using automatic method.