Application of Image Analytics for Tree enumeration for diversion of Forest Land

Accurate tree enumeration is essential for forest land diversion, environmental monitoring, and sustainable forestry management. Traditional methods rely on manual counting, which is time-consuming, labor-intensive, and prone to errors. This paper presents an automated tree enumeration system using advanced image analytics and deep learning models, including YOLOv8, YOLOv9, and YOLOv10. The system processes aerial and satellite images to detect, count, and classify trees with high accuracy. The backend, developed in Python, integrates OpenCV and TensorFlow for image processing and real-time object detection. The frontend, built using Streamlit, provides a user-friendly interface for image uploads and instant visualization of tree count results. By automating tree enumeration, this system significantly improves accuracy and efficiency, aiding environmental authorities, policymakers, and forest management professionals in making data-driven decisions for sustainable land use. Keywords Tree enumeration, Image analytics, YOLOv8, YOLOv9, YOLOv10, Deep learning, Object detection, Streamlit, Python, OpenCV, TensorFlow, Environmental monitoring, Forest management.