ReviewNature2025
Integrated biotechnological and AI innovations for crop improvement.
Review in Nature, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- OmniEdit: A unified CRISPR/Cas9 platform for precise genome engineering and strain optimization of theSynthetic and systems biotechnology · 2026Article
- Cell-based crop phenotyping for future climates.The New phytologist · 2026Review
- CasY7: An optimized Cas12i system for enhanced genome editing in monocot crops.Journal of integrative plant biology · 2026Article
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Designing rapid-cycling, compact architecture in tomato for vertical farming.Molecular horticulture · 2026Article
- Negative Regulators of Rice Agronomic Traits: Functional Insights and Applications in Genome Editing-Based Breeding.Plant biotechnology journal · 2026Review
- Metabolite-mediated plant immunity: From traditional defenders to artificial elicitors.Journal of integrative plant biology · 2026Review
- Editorial for the Special Issue "Advances in Multi-Omics for Functional Genomics Studies and Molecular Breeding".Current issues in molecular biology · 2026Article
- From Defense Executor to Engineering Target: Harnessing Lignin for Crop Resistance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrating 3D phenotyping and functional-structural plant models for crop ideotype breeding.Nature communications · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.Plant methods · 2026Article
- VE-MLM: A variable endmember-based multilinear mixing framework for crop FAPAR estimation using UAV multispectral imagery.Plant phenomics (Washington, D.C.) · 2026Article
- Review
- Plant3R: Fusing 3D feature learning with Gaussian splatting to enhance wheat plant 3D reconstruction precision.Plant phenomics (Washington, D.C.) · 2026Article
- Biotic Stress Resistance in Sweet Potato: Mechanisms, Perspectives, and Sustainable Production Strategies.Plants (Basel, Switzerland) · 2026Review
- Beyond Data: Artificial intelligence, knowledge graphs, and the next revolution in wheat 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crops provide food, clothing and other important products for the global population. To meet the demands of a growing population, substantial improvements are required in crop yield, quality and production sustainability. However, these goals are constrained by various environmental factors and limited genetic resources. Overcoming these limitations requires a paradigm shift in crop improvement by fully leveraging natural genetic diversity alongside biotechnological approaches such as genome editing and the heterologous expression of designed proteins, coupled with multimodal data integration. In this Review, we provide an in-depth analysis of integrated uses of omics technologies, genome editing, protein design and high-throughput phenotyping, in crop improvement, supported by artificial intelligence-enabled tools. We discuss the emerging applications and current challenges of these technologies in crop improvement. Finally, we present a perspective on how elite alleles generated through these technologies can be incorporated into the genomes of existing and de novo domesticated crops, aided by a proposed artificial intelligence model. We suggest that integrating these technologies with agricultural practices will lead to a new revolution in crop improvement, contributing to global food security in a sustainable manner.
Indexed as
Identifiers
40702261What OpenQuestion holds
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.