Palm Tree Detection

Palm tree detection research focuses on developing automated methods for identifying and locating palm trees in various environments, primarily using imagery from sources like drones and satellites. Current approaches leverage deep learning models, including convolutional neural networks (like ResNet) and object detection architectures (like YOLO), often incorporating transfer learning and probabilistic frameworks to improve accuracy and efficiency, particularly in challenging conditions like dense forests. This research is crucial for applications ranging from sustainable agriculture (e.g., pest management) to ecological monitoring and resource management, offering significant potential for improving efficiency and data acquisition in these fields.