Background
Accurate tree size measurement is essential for carbon credit calculation, yet traditional approaches depend on costly LIDAR sensors or time-consuming manual tape measurements. High-end iPhones support LIDAR, but their price and limited availability prevent large-scale adoption in forest projects.
Challenge
Carbon credit verification teams needed a low-cost, scalable method to measure tree circumference reliably in the field. The solution had to:
- Work on any smartphone—iOS or Android
- Deliver near-LIDAR accuracy
- Operate under real-world conditions (changing light, varied angles, dense forest)
Our Solution
Working with our customer, we developed a mobile application powered by machine learning and computer vision.
- Simple Workflow: Users take a photo of a tree with a small colored reference marker in view.
- Smart Detection: The app’s ML model analyzes the image and instantly calculates tree circumference.
- Automatic Logging: Each measurement is stored with GPS coordinates, timestamps, and user ID in a secure cloud database for immediate analysis.
Results & Impact
- Cross-Platform Access: Works on any modern smartphone—no LIDAR required.
- High Accuracy: Achieved measurement precision comparable to premium LIDAR devices in field tests.
- Operational Efficiency: Reduced field measurement time by over 70% compared to manual methods.
- Scalable Data Collection: Enables forestry teams to gather carbon credit data across large areas at minimal cost.
Key Takeaways
This project demonstrates that machine learning can deliver transformative results without expensive hardware. By combining practical mobile design with advanced ML algorithms, we created a scalable, low-cost solution for accurate tree measurement—empowering organizations to advance sustainability and carbon credit projects worldwide.