ReviewInternational journal of molecular sciences2022
Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Multi-Season Multi-Model GWAS Prioritizes Stable Genomic Loci and Candidate Genes for Six Agronomic Traits in Soybean Under Conditions in Southeastern Kazakhstan.Plants (Basel, Switzerland) · 2026Article
- Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.Journal of integrative plant biology · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Review
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Artificial intelligence in the mass production of natural enemies for biological control in modern agriculture.Pest management science · 2025Review
- The role of phosphatidylcholine metabolism in tumors.Medical oncology (Northwood, London, England) · 2025Review
- Antioxidant Defense Systems in Plants: Mechanisms, Regulation, and Biotechnological Strategies for Enhanced Oxidative Stress Tolerance.Life (Basel, Switzerland) · 2025Review
- Integrating genome editing with omics, artificial intelligence, and advanced farming technologies to increase crop productivity.Plant communications · 2025Review
- Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.Food chemistry: X · 2025Review
- Can the molecular and transgenic breeding of crops be an alternative and sustainable technology to meet food demand?Functional & integrative genomics · 2025Review
- Applications of CRISPR Technologies in Forestry and Molecular Wood Biotechnology.International journal of molecular sciences · 2024Review
- Leveraging multi-omics tools to comprehend responses and tolerance mechanisms of heavy metals in crop plants.Functional & integrative genomics · 2024Review
- Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review.Plants (Basel, Switzerland) · 2024Review
- Current status of molecular rice breeding for durable and broad-spectrum resistance to major diseases and insect pests.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Review
- Radiation synthesis of sodium alginate/gelatin based ultra-absorbent hydrogel for efficient water and nitrogen management in wheat under drought stress.Scientific reports · 2024Article
- Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity.Plant cell reports · 2024Review
- How Plants Tolerate Salt Stress.Current issues in molecular biology · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other "omics" approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These "omics" approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with "omics" tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
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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.