ReviewiScience2021
Machine learning in plant science and plant breeding.
Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 2 of them syntheses that pooled it.
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
105 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for multi-omics data integration in crop improvement: a systematic review.BMC bioinformatics · 2026Pooled it
- Multi-omics revolution to promote plant breeding efficiency.Frontiers in plant science · 2022Pooled it
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Evaluating Causal Claims in Plant Developmental Metabolism: A When-Where-How Framework.Plants (Basel, Switzerland) · 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
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Challenges in Bringing Pangenome Research Into Breeding: A Case Study in Rice.Plant biotechnology journal · 2026Review
- From fragrance wheel to functional genes: a multi-omics investigation into fragrance type formation in ornamentalHorticulture research · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- The emerging impact of CRISPR and gene editing on global crop improvement.Transgenic research · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Physiological determinants of heterosis in hybrid rice (Frontiers in plant science · 2026Article
- Decades' progress and prospects on maize functional genomics and molecular breeding.Science China. Life sciences · 2025Review
- Sucrose as a key nutritional marker distinguishing vegetable and grain soybeans, regulated byHorticulture research · 2025Article
- Perspectives on morphology, physiology, genetic polymorphism and machine learning in cucumber grafting under zinc toxicity.BMC plant biology · 2025Article
- Molecular Network Analysis in Model and Non-Model Legumes: Challenges in Omics Data Interpretation Across Species, with a Focus onPlants (Basel, Switzerland) · 2025Review
- 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
- Merging traditional practices and modern technology through computational plant breeding.Plant physiology · 2025Review
45 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.
Indexed as
Identifiers
What 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.