Integrating artificial intelligence to support systemic advances in silviculture
DOI:
https://doi.org/10.12899/asr-2681Keywords:
Forest management, complex adaptive systems, machine learning, deep learning, reinforcement learning, computer vision, natural language processing, decision support systemsAbstract
Silviculture is playing an increasingly vital role in addressing global environmental challenges such as climate change, biodiversity loss and the rising demand for forest resources. In this context, the integration of artificial intelligence (AI) into forestry practices has recently emerged as a promising pathway. The potential benefits are substantial: AI offers the promise of greater operational efficiency and more informed, data-driven decision-making. However, the transformative potential of AI extends well beyond the mere automation of existing processes. It may contribute to a shift in the conventional management paradigm: from decision-making based on static, periodically updated plans to an approach where decisions are continuously informed and refined by real-time data streams and model outputs. This evolution supports the emergence of truly adaptive silviculture, aligned with the principles of complex adaptive systems. On the other hand, the effective integration of AI into silviculture also presents notable limitations and research challenges. Key issues include the need for robust AI models tailored to the intricacies of dynamic forest ecosystems, the development of cost-effective methods for data acquisition and management, the advancement of explainable AI for greater transparency and trust and the careful consideration of the ethical, social and economic implications associated with AI adoption in forest management. This note explores these subjects through a commented discussion.
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