Sustainable Forestry: AI-Driven Forest Health Diagnostics using Swin Transformer

Forestry has a significant role in preserving and protecting biodiversity and maintaining a healthy environment. Regular monitoring of forest health is essential to detect early signs of tree degradation and mitigate risks like forest fires. This study proposes a method through deep learning for diagnosing forest health using the SWIN Transformer model, which leverages UAV-captured high-resolution images. By classifying trees into categories—healthy, lightly damaged, heavily damaged, and others—our model offers a scalable solution for large-scale forest monitoring. Test results show that SWIN Transformer is better than the standard, providing forest managers with a reliable tool for ecosystem management and forest fire risk assessment. Our analysis reveals a continued decline in forest health, marked by a decreasing number of healthy trees and an increasing number of damaged trees succumbing to death. By analysing temporal image data, we can accurately track changes in forest health, providing valuable insights into the rate of decline or improvement over time. Our model SWIN Transformer accurately finds and labels tree conditions 93% of the time. This lets us precisely count the number of dead and living trees, which is a key part of diagnosing forest health.