Agronomic and Genetic Research of Crops Empowered by Artificial Intelligence
A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".
Deadline for manuscript submissions: 20 September 2026 | Viewed by 200
Special Issue Editors
Interests: AI in agriculture; Gaussian splatting; high-throughput phenotyping; genomic selection; precision design breeding; biological big data
Interests: genomic-enhanced breeding; breeding tool development
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI), particularly agricultural foundation models and large-scale data-driven learning frameworks, into crop agronomic management, phenotypic extraction, and genetic improvement has emerged as a major frontier in modern agricultural research. Agronomic studies encompass crop physiology, phenotypic trait characterization, yield formation and stability, and regional productivity differences under diverse environmental conditions. Genetic and molecular research focuses on gene discovery, functional characterization, regulatory network construction, and multi-omics analyses—including genomics, transcriptomics, proteomics, and metabolomics—to elucidate the mechanisms underlying complex agronomic traits and their links to observable plant phenotypes.
This Special Issue, “Agronomic and Genetic Research of Crops Empowered by Artificial Intelligence”, aims to showcase how AI-driven methodologies, including large-scale agricultural foundation models and multimodal learning paradigms, advance crop research across multiple biological scales, from genes to fields. In particular, the scope includes (but is not limited to) foundation-model-assisted transcriptomic analyses and regulatory network inference for abiotic stress tolerance, functional genomics and hormone-responsive pathways, yield variation and productivity analysis of major crops across regions, intelligent identification and monitoring of crop pests and diseases, and cross-scale integration of omics, phenotypic, environmental, and management data.
We welcome original research and reviews that demonstrate the development and application of machine learning, deep learning, and agricultural foundation models for multi-omics data integration, gene function prediction, stress-response modeling, yield prediction, pest and disease recognition, high-throughput phenotyping, and precision agronomic decision-making. Studies emphasizing pre-trained models, transfer learning, multimodal representation learning, and generalizable AI systems for crop science are particularly encouraged. By bridging advanced AI methodologies with agronomic and genetic research, this Special Issue seeks to promote the development of high-yielding, stress-resilient, and climate-adaptive crop systems to support sustainable agriculture and global food security.
Dr. Weifu Li
Prof. Dr. Hongwei Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- AI in agriculture
- precision design breeding
- multi-omics
- high-throughput phenotyping
- stress resilience
- foundation models
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