Convolutional Neural Network Competence in Image Analytics for Tree Enumeration

Abstract

Tree identification and counting are crucial tasks for forest management, conservation, and planning. Traditional methods, such as manual surveys, are time-consuming, costly, and prone to errors. This paper aims at developing an efficient system for an automated tree detection and counting with the model of YOLO (You Only Look Once). Proposed approach uses annotated dataset from Rob flow and geographical data to improve accuracy and efficiency in tree detection. This YOLO method is optimized for the precise instance of tree detection. This technique comprises new approaches to candidate positive sample selection, which have a new design specific to small and medium-sized tree. The parameters of the YOLOv8 model will be optimized to have a high F1 score and recall rate so that the correct trees are identified. The evaluation metrics are F1 score, precision, and recall to balance the false positives with false negatives. This will streamline the environmental assessments by automatically identifying and counting trees, thereby giving a clear indication of how well managed the forest is. Such innovation demonstrates how integration of machine learning techniques with advanced data sources could be very important in enhancing the tree detection and counting task.