International Research Journal of Education and Technology

Automated tree enumeration is an essential tool for modern forestry, environmental monitoring, and urban planning. This project presents an innovative solution leveraging image analytics to accurately count and classify trees in diverse environments. By combining state-of-the-art machine learning algorithms with advanced image processing techniques, our system can process high-resolution aerial or satellite images to identify tree canopies and determine their respective locations and counts. The core of our solution employs convolutional neural networks (CNNs) for feature extraction, segmentation, and object recognition. The model has been fine-tuned to handle varying tree densities, overlapping canopies, and diverse lighting conditions, ensuring reliable performance in real-world scenarios. An additional geospatial mapping module integrates the extracted data, enabling precise mapping of tree distributions. Field testing and validation have demonstrated significant accuracy improvements compared to traditional manual and semi-automated methods. Our solution processes images in a fraction of the time required by conventional techniques, offering scalability for large-scale forestry management and conservation efforts. This application addresses key challenges such as the increasing demand for accurate and efficient forest resource assessments and the growing need for environmental sustainability. Furthermore, it provides critical data for carbon sequestration analysis, biodiversity studies, and urban greening projects.

The project also emphasizes user-centric features, including an intuitive interface and customizable analytics dashboards for stakeholders. With its focus on automation, accuracy, and scalability, this solution represents a pivotal advancement in remote sensing and environmental management technologies. Future work includes enhancing tree species classification capabilities and adapting the system to track temporal changes in forested regions.