ArticleJournal of integrative plant biology2026
Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.
Article in Journal of integrative plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Heat stress responses of plants during adaptation to high temperature environments: A review of recent advances and insights.Plant signaling & behavior · 2026Review
- Cotton Seed Germination Under the Main Abiotic Stress: Physiological Mechanisms, Molecular Responses, and Agronomic Strategies.Plants (Basel, Switzerland) · 2026Review
- Transgene-free genome editing in plants.aBIOTECH · 2026Review
- Faster, Smarter, Precise: Integrating Speed Breeding and CRISPR-Based New Genomic Techniques into the Conventional Field Crop Breeding Pipeline.Plants (Basel, Switzerland) · 2026Review
- Hybrid sorghum breeding in China: A historical review and perspectives.Journal of integrative plant biology · 2026Review
- Melatonin seed priming: A climate-smart, green strategy to enhance abiotic stress tolerance in plants.Journal of integrative plant biology · 2026Review
- Climate Change and African Agricultural Resilience: A 10-Year Comprehensive Review of Adaptive Capacity, Policy, and Environmental Stewardship.Plant-environment interactions (Hoboken, N.J.) · 2026Review
- Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.Journal of integrative plant biology · 2026Article
- Plant biology for a changing world: Expert reviews on crop resilience, breeding, and emerging technologies.Journal of integrative plant biology · 2026Article
- The Rice-Xoc Arms Race: Molecular Mechanisms and Genomic Strategies Against Bacterial Leaf Streak.Rice (New York, N.Y.) · 2026Review
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Advances in molecular breeding of medicinal plants.Molecular horticulture · 2026Review
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- From signals to solutions: stress-induced leaf senescence and synthetic biology and AI approaches for crop resilience.Molecular horticulture · 2026Review
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026Review
- Microalgae-Driven Circular Agriculture: System Integration, Nutrient Recovery, and AI-Assisted Optimization.Microorganisms · 2026Review
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Crop breeding technologies are vital for global food security. While traditional methods have improved yield, stress tolerance, and nutrition, rising challenges such as climate instability, land loss, and pest pressure now demand new solutions. This study introduces the Breeding 5.0 framework, driven by artificial intelligence (AI) and robotics, marking a shift from empirical selection to intelligent systems. Central to this transformation is AI's emerging ability to deeply "understand germplasm", not merely by identifying genetic markers but also by decoding its architecture, plasticity, regulatory logic, and environmental interactions. This germplasm intelligence enables predictive trait modeling, optimized parental design, and targeted selection. We define four technical paradigms enabling this shift: (i) Multimodal data integration to bridge genotype and phenotype; (ii) Omni-simulated environments for virtual performance testing; (iii) Peopleless data capture for scalable precision; and (iv) Expert, explainable AI for biologically grounded decisions. Together, these technologies algorithmically convert germplasm into actionable breeding insights, accelerating the full cycle from ideal plant type design to elite line development. We further propose the "breeding flywheel", a self-reinforcing system that continuously amplifies phenotypic gains and refines breeding strategies, thereby enabling faster and smarter crop improvement to ensure a sustainable food future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.