Traditional tree counting relied on field surveys or semi-automated methods, such as NDVI thresholding and clustering. These methods are slow, labor-intensive, and often inaccurate in cases of dense or overlapping canopies. Even recently, most of the deep learning-based solutions have focused on detecting trees only, without proactive monitoring, long-term vegetation analysis, or policy integrations.GreenCount overcomes these limitations through an AI-driven system combining computer vision, image analytics, and deep learning that count trees with high accuracy and scalability. It is built using TensorFlow, PyTorch, and OpenCV; it follows a structured pipeline of preprocessing, segmentation, and canopy detection; and it provides high accuracy across diverse environments. It then adds historical imagery analysis for monitoring long-term vegetation changes, and it translates technical outputs into actionable insights through dashboards for policymakers and conservation teams.Interoperable by design, GreenCount can be integrated with government environmental portals to enable transparent and evidence-based decision-making. It cuts manual effort by more than 70%, increases accuracy to 93–95% in dense canopies, and processes imagery at almost five times the speed. Integrating the detection of real-time alerts and change tracking into a unified system, GreenCount offers a holistic and policy-relevant solution to sustainable environmental management.
Tarade et al. (Thu,) studied this question.