A Survey on Automated Palm Tree Identification from Traditional Machine Learning and Deep Learning Perspectives

This paper reviews automated palm tree identification methods based on traditional machine learning and deep learning, exploring various data sources (e.g., UAV, satellite, and aerial imagery) and imaging modalities (RGB, thermal, multispectral, and LiDAR). It systematically analyzes the strengths and limitations of segmentation, classification, and object detection algorithms, highlighting their practical applications in agriculture, ecological conservation, and urban planning. The study demonstrates that deep learning approaches (e.g., CNNs and Vision Transformers) excel in complex environments and large-scale datasets but face challenges such as species similarity, environmental variability, and computational constraints. Future research should focus on multimodal data fusion, lightweight model development, and dataset expansion.