ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2021
Predicting phenotypes from genetic, environment, management, and historical data using CNNs.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- 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
- Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Optimizing genomic prediction with transfer learning under a ridge regression framework.The plant genome · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Machine Learning-Driven Identification of Key Environmental Factors Influencing Fiber Yield and Quality Traits in Upland Cotton.Plants (Basel, Switzerland) · 2025Article
- EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models.Briefings in bioinformatics · 2025Article
- Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.Plant biotechnology journal · 2025Article
- Fast-forwarding plant breeding with deep learning-based genomic prediction.Journal of integrative plant biology · 2025Review
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 2025Article
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Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
key messageConvolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance.
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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.