ReviewGenes2023
Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs.
Review in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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.
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.
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Who cites it
35 citing papers in PubMed.
- Application of Omics Technologies for Cowpea Improvement.Plant-environment interactions (Hoboken, N.J.) · 2026Review
- Cross incompatibility restricting genome exchange and diversification of primary gene pool in plants: challenges and opportunities.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.G3 (Bethesda, Md.) · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Casein genotypes associate with baseline and dynamic regression components of seminal quality in Murciano-Granadina bucks.Scientific reports · 2026Article
- Review
- Statistics and data science unlock the predictive power of quantitative genetics.Frontiers in plant science · 2026Review
- Accelerated breeding modernization: a global blueprint for driving genetic gains, climate resilience, and food security in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.Plant molecular biology · 2025Review
- Molecular Breeding for Abiotic Stress Tolerance in Crops: Recent Developments and Future Prospectives.International journal of molecular sciences · 2025Review
- Integrative Machine Learning Approaches for Identifying Loci Associated with Anthracnose Resistance in Strawberry.Plants (Basel, Switzerland) · 2025Article
- Merging traditional practices and modern technology through computational plant breeding.Plant physiology · 2025Review
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Genomics-assisted breeding for designing salinity-smart future crops.Plant biotechnology journal · 2025Review
- Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.Food chemistry: X · 2025Review
- Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches.Scientific reports · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Machine Learning-Based identification of resistance genes associated with sunflower broomrape.Plant methods · 2025Article
- Flavonoids as key players in cold tolerance: molecular insights and applications in horticultural crops.Horticulture research · 2025Article
- Cell Wall-Based Machine Learning Models to Predict Plant Growth Using Onion Epidermis.International journal of molecular sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.
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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.