Tree Enumeration Using Image Analytics

Accurate tree enumeration is essential for ecological monitoring, urban planning, and forest management. Traditionally, this task has been carried out manually, which is not only time-consuming but also prone to human error. This project aims to simplify and automate the process of tree counting using image analytics. By leveraging a deep learning-based object detection model—YOLOv8—we trained the system to detect and count trees from aerial and landscape images. The approach involves curating a custom dataset, annotating it using tools like Roboflow, and training the model on Google Colab. A simple web interface was developed using Flask to allow users to upload an image and receive real-time results showing the number and type of trees detected. The model performed well on various test images, showing a high detection accuracy. This system not only reduces manual effort but also provides a scalable and efficient solution for large-scale environmental data collection