With the continued innovation of smart forestry, the convergence of artificial intelligence (AI) and multi-omics technologies is emerging as a critical driver for advancing forest tree improvement and sustainable forest management. This special issue centers on AI-enabled integration and modeling of phenomic, genomic, transcriptomic, proteomic, and enviromic datasets, with the objective of dissecting the molecular and regulatory bases of complex traits and supporting the development of precise, data-driven breeding strategies.

The increasing availability of high-throughput phenotyping platforms—such as UAV-based multispectral and hyperspectral imaging and LiDAR—together with comprehensive environmental data, provides a robust foundation for the application of modern AI methodologies. Deep learning architectures and multimodal fusion frameworks have demonstrated substantial potential for early, accurate, and scalable prediction of phenotypes in large forest tree populations, enabling diverse applications including health diagnostics, growth and productivity assessment, wood-quality inference, and abiotic/biotic stress evaluation.

A major focus of this special issue is the deployment of AI in quantative genetics and functional genomics. AI-augmented genome-wide association studies (GWAS) and genomic prediction (GP) are improving the resolution and efficiency of elite genotype identification, thereby facilitating a more quantitative and predictive transition from genotype to phenotype. At the molecular level, AI-driven approaches for transcription-factor function inference, gene regulatory network reconstruction, and prioritization of candidate genes are becoming indispensable for elucidating pathways involved in wood formation, metabolic regulation (e.g., resin biosynthesis), and adaptive responses to environmental stressors.

In addition, this special issue emphasizes the emerging role of AI in supporting gene-engineering approaches for forest tree improvement. Examples include AI-assisted design and evaluation of CRISPR targets to enhance editing precision and reduce experimental burden, as well as multi-omics-guided construction of trait-associated gene–regulatory modules that inform the development of resilient and climate-adaptive germplasm.

Topics of interest include, but are not limited to:

• AI methodologies for multi-omics data integration and trait-prediction modeling

• High-throughput phenotyping using UAVs, robots, LiDAR, multispectral and hyperspectral imaging

• AI-enhanced GWAS and genomic selection

• Transcription-factor function inference, regulatory network reconstruction, and systems-biology approaches

• AI-assisted optimization of CRISPR gene editing and transgenic strategies

• Enviromics-based modeling of climate adaptation and stress resilience

• Empirically validated AI applications within operational smart-forestry breeding pipelines

By advancing the methodological and practical integration of AI, multi-omics, and gene-engineering technologies, this special issue aims to establish rigorous, reproducible, and scalable frameworks for precision tree breeding, thereby promoting the development of more resilient, productive, and sustainable forest ecosystems.

Guest Editors:

Zhi-Qiang Chen, Nanjing Forestry University. 

Lei Zhou, College of Mechanical and Electronic Engineering, Nanjing Forestry University.

Nan Lu, Research Institute of Forestry, Chinese Academy of Forestry. 

Xianyin Ding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry.

Deadline

The deadline for manuscript submissions is 31 July 2026, but we can accommodate extensions on a case-by-case basis. All papers will be published as open access articles upon acceptance.

Submission Instructions

Please submit the full manuscript to Smart Forestry via our Online Submission System. All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors for submission of manuscripts is available on the For Authors page.

Additionally, please choose the topic of this Special Issue when submitting and specify it in your cover letter. For further inquiries, please contact Guest Editors:

Zhi-Qiang Chen (zhiqiang_chen@njfu.edu.cn)

Lei Zhou (leizhou@njfu.edu.cn)

Nan Lu (ln_890110@163.com)

Xianyin Ding (brighten@caf.ac.cn)