Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects

Artificial intelligence (AI) is a field within computer science that is increasingly applied across a wide range of industries. Global climate change and human activity are leading to deforestation, which can have serious ecological and economic consequences. One way to conserve natural forest resources is to create high-yielding and stress-tolerant varieties of tree species with the desired quality characteristics of raw materials using biotechnological breeding methods. In this review, we summarize the achievements and current status of research on the application of AI in forest biotechnology. We examine machine learning algorithms and artificial neural network architectures with respect to their use in various areas of forest biotechnology: in vitro culture, transgenic plants, genome editing, omics technologies, and genomic selection. The review discusses challenges specific to woody plants, such as the deficiency of datasets for model training, as well as the ethical aspects of AI use, including interpretability, bias, and accountability. Finally, we suggest future research directions for consideration. This review may be useful for AI specialists, researchers in plant sciences, forestry practitioners, and policymakers to comprehensively understand the role of AI technologies in investigating and improving forest trees.