Sustainable Solutions: Enhancing Tree Enumeration for Informed Land Use Decisions

Forest land is being diverted due to the rising need for resource extraction and land development, which poses significant global risks to ecological balance and biodiversity. The quick diversion of forest land demands efficient tree enumeration techniques. The accuracy and time-consuming nature of traditional tree enumeration methods underscores the need for better, more innovative approaches. Advanced computer vision technologies have the potential to improve environmental monitoring and management in the field of forest conservation. This paper investigates how these advanced technologies, particularly the YOLO (You Only Look Once) framework, can be used to improve and facilitate the tree enumeration process, incorporating object detection methodologies. An enhanced model for effectively detecting tree populations in forested areas can be developed by utilizing neural network architectures like YOLOv8, the most recent version of the YOLO framework. The integration of deep learning algorithms within the YOLOv8 framework facilitates accurate tree detection and enumeration in forest land areas. The proposed model based on YOLOv8 achieved an accuracy of 98.37 percent, demonstrating the potential of this approach in enhancing precision and efficiency in tree enumeration. By enabling informed decisions on sustainable land use and conservation, our proposed methodology significantly contributes to environmental monitoring and management efforts.