Harnessing Artificial Intelligence, Machine Learning and Deep Learning for Sustainable Forestry Management and Conservation: Transformative Potential and Future Perspectives

Taojing Wang, Yinyue Zuo, Teja Manda, Delight Hwarari, Liming Yang

Plants serve as the basis for ecosystems and provide a wide range of essential ecological, environmental, and economic benefits. However, forest plants and other forest systems are constantly threatened by degradation and extinction, mainly due to misuse and exhaustion. Therefore, sustainable forest management (SFM) is paramount, especially in the wake of global climate change and other challenges. SFM ensures the continued provision of plants and forests to both the present and future generations. In practice, SFM faces challenges in balancing the use and conservation of forests. This review discusses the transformative potential of artificial intelligence (AI), machine learning, and deep learning (DL) technologies in sustainable forest management. It summarizes current research and technological improvements implemented in sustainable forest management using AI, discussing their applications, such as predictive analytics and modeling techniques that enable accurate forecasting of forest dynamics in carbon sequestration, species distribution, and ecosystem conditions. Additionally, it explores how AI-powered decision support systems facilitate forest adaptive management strategies by integrating real-time data in the form of images or videos. The review manuscript also highlights limitations incurred by AI, ML, and DL in combating challenges in sustainable forest management, providing acceptable solutions to these problems. It concludes by providing future perspectives and the immense potential of AI, ML, and DL in modernizing SFM. Nonetheless, a great deal of research has already shed much light on this topic, this review bridges the knowledge gap.