{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:38:44Z","timestamp":1787236724479,"version":"build-2736575974"},"reference-count":160,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.<\/jats:p>","DOI":"10.3389\/fpls.2026.1929693","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:06:25Z","timestamp":1787234785000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-integrated digital breeding for crop improvement"],"prefix":"10.3389","volume":"17","author":[{"given":"Seungmin","family":"Son","sequence":"first","affiliation":[{"name":"National Institute of Agricultural Sciences, Rural Development Administration","place":["Jeonju, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Ryeol","family":"Park","sequence":"additional","affiliation":[{"name":"National Institute of Agricultural Sciences, Rural Development Administration","place":["Jeonju, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1093\/plcell\/koac079","article-title":"Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning","volume":"34","author":"Akagi","year":"2022","journal-title":"Plant Cell"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1093\/pcp\/pcaa111","article-title":"Explainable deep learning reproduces a 'professional eye' on the diagnosis of internal disorders in persimmon fruit","volume":"61","author":"Akagi","year":"2020","journal-title":"Plant Cell Physiol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.molp.2024.03.007","article-title":"Genomic selection in plant breeding: Key factors shaping two decades of progress","volume":"17","author":"Alemu","year":"2024","journal-title":"Mol. Plant"},{"key":"B4","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/s41592-021-01197-1","article-title":"Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices","volume":"18","author":"Alseekh","year":"2021","journal-title":"Nat. Methods"},{"key":"B5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s13059-020-02015-1","article-title":"MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1105\/tpc.19.00332","article-title":"Transcriptome-based prediction of complex traits in maize","volume":"32","author":"Azodi","year":"2020","journal-title":"Plant Cell"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf355","article-title":"A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches","volume":"26","author":"Baiao","year":"2025","journal-title":"Brief Bioinform."},{"key":"B8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41438-019-0151-5","article-title":"Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: a case study of lettuce production","volume":"6","author":"Bauer","year":"2019","journal-title":"Hortic. Res."},{"key":"B9","unstructured":"Bayer\n          New Technologies Driving the Future of Plant Breeding\n          \n          2023"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0276972","article-title":"A deep learning generative model approach for image synthesis of plant leaves","volume":"17","author":"Benfenati","year":"2022","journal-title":"PLoS One"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-15-646","article-title":"The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye","volume":"15","author":"Bernal-Vasquez","year":"2014","journal-title":"BMC Genomics"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2020.583438","article-title":"Improving image-based plant disease classification with generative adversarial network under limited training set","volume":"11","author":"Bi","year":"2020","journal-title":"Front. Plant Sci."},{"key":"B13","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s13593-020-00630-7","article-title":"Data synthesis for crop variety evaluation. A review","volume":"40","author":"Brown","year":"2020","journal-title":"Agron. Sustain. Dev."},{"key":"B14","doi-asserted-by":"publisher","first-page":"707","DOI":"10.2135\/cropsci2011.06.0299","article-title":"Genomic prediction of breeding values when modeling genotype\u00d7 environment interaction using pedigree and dense molecular markers","volume":"52","author":"Burgue\u00f1o","year":"2012","journal-title":"Crop Sci."},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz126","article-title":"Access to RNA-sequencing data from 1,173 plant species: The 1000 Plant transcriptomes initiative (1KP)","volume":"8","author":"Carpenter","year":"2019","journal-title":"GigaScience"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3390\/biology12101298","article-title":"An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture","volume":"12","author":"Cembrowska-Lech","year":"2023","journal-title":"Biol. (Basel)"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1111\/ppl.70572","article-title":"The invisible frontline: High-tech root imaging for crop stress adaptation","volume":"177","author":"Chandnani","year":"2025","journal-title":"Physiol. Plant"},{"key":"B18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.tplants.2024.09.011","article-title":"Machine learning algorithms translate big data into predictive breeding accuracy","volume":"30","author":"Crossa","year":"2025","journal-title":"Trends Plant Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1016\/j.tplants.2017.08.011","article-title":"Genomic selection in plant breeding: Methods, models, and perspectives","volume":"22","author":"Crossa","year":"2017","journal-title":"Trends Plant Sci."},{"key":"B20","doi-asserted-by":"publisher","first-page":"5676","DOI":"10.1016\/j.csbj.2023.11.025","article-title":"DNABERT-based explainable lncRNA identification in plant genome assemblies","volume":"21","author":"Danilevicz","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B21","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1038\/s42003-025-07890-7","article-title":"Breaking the field phenotyping bottleneck in maize with autonomous robots","volume":"8","author":"DeBruin","year":"2025","journal-title":"Commun. Biol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"baad088","DOI":"10.1093\/database\/baad088","article-title":"Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences","volume":"2023","author":"Deng","year":"2023","journal-title":"Database (Oxford)"},{"key":"B23","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1534\/g3.119.400759","article-title":"Novel Bayesian networks for genomic prediction of developmental traits in biomass sorghum","volume":"10","author":"Dos Santos","year":"2020","journal-title":"G3 (Bethesda)"},{"key":"B24","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s13007-024-01205-3","article-title":"Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks","volume":"20","author":"Drees","year":"2024","journal-title":"Plant Methods"},{"key":"B25","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/s13007-022-00970-3","article-title":"CropPainter: an effective and precise tool for trait-to-image crop visualization based on generative adversarial networks","volume":"18","author":"Duan","year":"2022","journal-title":"Plant Methods"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.21105\/joss.06368","article-title":"agweather-qaqc: An interactive Python package for quality assurance and quality control of daily agricultural weather data and calculation of reference evapotranspiration","volume":"9","author":"Dunkerly","year":"2024","journal-title":"J. Open Source Software"},{"key":"B27","doi-asserted-by":"publisher","first-page":"250","DOI":"10.3835\/plantgenome2011.08.0024","article-title":"Ridge regression and other kernels for genomic selection with R package rrBLUP","volume":"4","author":"Endelman","year":"2011","journal-title":"Plant Genome"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2023.1098308","article-title":"Missing data in multi-omics integration: Recent advances through artificial intelligence","volume":"6","author":"Flores","year":"2023","journal-title":"Front. Artif. Intell."},{"key":"B29","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1111\/jipb.70008","article-title":"Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation","volume":"68","author":"Fu","year":"2026","journal-title":"J. Integr. Plant Biol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/pcp\/pcab173","article-title":"Development of RIKEN plant metabolome metadatabase","volume":"63","author":"Fukushima","year":"2022","journal-title":"Plant Cell Physiol."},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1093\/hr\/uhaf128","article-title":"Deep learning empowers genomic selection of pest-resistant grapevine","volume":"12","author":"Gan","year":"2025","journal-title":"Hortic. Res."},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0064","article-title":"Enhancing green fraction estimation in rice and wheat crops: A self-supervised deep learning semantic segmentation approach","volume":"5","author":"Gao","year":"2023","journal-title":"Plant Phenomics"},{"key":"B33","doi-asserted-by":"publisher","first-page":"4613","DOI":"10.1073\/pnas.1716999115","article-title":"An explainable deep machine vision framework for plant stress phenotyping","volume":"115","author":"Ghosal","year":"2018","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"B34","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1534\/genetics.109.103952","article-title":"Additive genetic variability and the Bayesian alphabet","volume":"183","author":"Gianola","year":"2009","journal-title":"Genetics"},{"key":"B35","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1186\/s12870-022-03559-z","article-title":"Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction","volume":"22","author":"Gill","year":"2022","journal-title":"BMC Plant Biol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s12711-023-00835-w","article-title":"Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley","volume":"55","author":"Guo","year":"2023","journal-title":"Genet. Sel. Evol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1017\/S1479262125100427","article-title":"Identifying gaps and challenges for conserving crop diversity in genebanks: Lessons from the reviews of national genebanks","volume":"24","author":"Hanson","year":"2025","journal-title":"Plant Genet. Resour."},{"key":"B38","doi-asserted-by":"publisher","first-page":"D1606","DOI":"10.1093\/nar\/gkae991","article-title":"Plant metabolic network 16: Expansion of underrepresented plant groups and experimentally supported enzyme data","volume":"53","author":"Hawkins","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"B39","doi-asserted-by":"publisher","first-page":"D1629","DOI":"10.1093\/nar\/gkad706","article-title":"scPlantDB: a comprehensive database for exploring cell types and markers of plant cell atlases","volume":"52","author":"He","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.986856","article-title":"4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography","volume":"13","author":"Herrero-Huerta","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2024.101038","article-title":"PCMD: A multilevel comparison database of intra- and cross-species metabolic profiling in 530 plant species","volume":"5","author":"Hu","year":"2024","journal-title":"Plant Commun."},{"key":"B42","doi-asserted-by":"publisher","first-page":"7333","DOI":"10.1038\/s41598-024-58049-z","article-title":"Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling","volume":"14","author":"Ibrar","year":"2024","journal-title":"Sci. Rep."},{"key":"B43","unstructured":"International Rice Research Institute\n          IRRI Launches AI-Powered Global Digital Platform for Hybrid Rice Breeding and Parental Selection\n          \n          2025"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2020.00944","article-title":"Systematic multi-omics integration (MOI) approach in plant systems biology","volume":"11","author":"Jamil","year":"2020","journal-title":"Front. Plant Sci."},{"key":"B45","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s00122-013-2243-1","article-title":"A reaction norm model for genomic selection using high-dimensional genomic and environmental data","volume":"127","author":"Jarquin","year":"2014","journal-title":"Theor. Appl. Genet."},{"key":"B46","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1146\/annurev-arplant-090823-015358","article-title":"What are we learning from plant pangenomes","volume":"76","author":"Jayakodi","year":"2025","journal-title":"Annu. Rev. Plant Biol."},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1092584","article-title":"Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction","volume":"14","author":"Jeon","year":"2023","journal-title":"Front. Plant Sci."},{"key":"B48","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1534\/genetics.112.144246","article-title":"Multiple-trait genomic selection methods increase genetic value prediction accuracy","volume":"192","author":"Jia","year":"2012","journal-title":"Genetics"},{"key":"B49","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1074\/mcp.RA118.001021","article-title":"Characterization of proteome variation during modern maize breeding","volume":"18","author":"Jiang","year":"2019","journal-title":"Mol. Cell. Proteomics"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1491493","article-title":"An explainable Bi-LSTM model for winter wheat yield prediction","volume":"15","author":"Joshi","year":"2025","journal-title":"Front. Plant Sci."},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1093\/hr\/uhae319","article-title":"Integrative multi-environmental genomic prediction in apple","volume":"12","author":"Jung","year":"2025","journal-title":"Hortic. Res."},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.3390\/plants13091262","article-title":"RGB imaging as a tool for remote sensing of characteristics of terrestrial plants: a review","volume":"13","author":"Kior","year":"2024","journal-title":"Plants"},{"key":"B53","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B54","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s40659-024-00562-6","article-title":"Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing","volume":"57","author":"Kumar","year":"2024","journal-title":"Biol. Res."},{"key":"B55","unstructured":"KWS\n          The Perfect Pepper in View\n          \n          2024"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-559","article-title":"WGCNA: an R package for weighted correlation network analysis","volume":"9","author":"Langfelder","year":"2008","journal-title":"BMC Bioinf."},{"key":"B57","doi-asserted-by":"publisher","first-page":"400","DOI":"10.7740\/kjcs.2024.69.4.400","article-title":"Supercomputer-based large-scale ultra-high-speed genotyping of crop genomes","volume":"69","author":"Lee","year":"2024","journal-title":"Korean J. Crop Sci."},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.3390\/s25113401","article-title":"A review of optical-based three-dimensional reconstruction and multi-source fusion for plant phenotyping","volume":"25","author":"Li","year":"2025","journal-title":"Sensors (Basel)"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1757949","article-title":"Advances and challenges in plant molecular marker technologies and their applications in the artificial intelligence empowered era","volume":"16","author":"Li","year":"2026","journal-title":"Front. Plant Sci."},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2020.623705","article-title":"High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field","volume":"8","author":"Li","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1002\/advs.75772","article-title":"PlantGFM: a genomic foundation model for discovery and creation of plant genes","author":"Li","year":"2026","journal-title":"Adv. Sci."},{"key":"B60","doi-asserted-by":"publisher","first-page":"20078","DOI":"10.3390\/s141120078","article-title":"A review of imaging techniques for plant phenotyping","volume":"14","author":"Li","year":"2014","journal-title":"Sensors (Basel)"},{"key":"B63","doi-asserted-by":"publisher","first-page":"D1483","DOI":"10.1093\/nar\/gkac917","article-title":"PlantExp: a platform for exploration of gene expression and alternative splicing based on public plant RNA-seq samples","volume":"51","author":"Liu","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1093\/g3journal\/jkab383","article-title":"Genome-wide imputation using the practical haplotype graph in the heterozygous crop cassava","volume":"12","author":"Long","year":"2022","journal-title":"G3 (Bethesda)"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol."},{"key":"B66","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1093\/jxb\/erad384","article-title":"Metabolomics to understand metabolic regulation underpinning fruit ripening, development, and quality","volume":"75","author":"Martinez-Rivas","year":"2024","journal-title":"J. Exp. Bot."},{"key":"B67","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1038\/s41588-019-0443-6","article-title":"Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding","volume":"51","author":"Mascher","year":"2019","journal-title":"Nat. Genet."},{"key":"B68","first-page":"12","article-title":"A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955","volume":"27","author":"McCarthy","year":"2006","journal-title":"AI Magazine"},{"key":"B69","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1111\/ele.13610","article-title":"Uncovering ecological state dynamics with hidden Markov models","volume":"23","author":"McClintock","year":"2020","journal-title":"Ecol. Lett."},{"key":"B70","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1038\/s41586-020-2094-2","article-title":"Mass-spectrometry-based draft of the Arabidopsis proteome","volume":"579","author":"Mergner","year":"2020","journal-title":"Nature"},{"key":"B71","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1093\/genetics\/157.4.1819","article-title":"Prediction of total genetic value using genome-wide dense marker maps","volume":"157","author":"Meuwissen","year":"2001","journal-title":"Genetics"},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.832749","article-title":"Dissecting breeders' sense via explainable machine learning approach: application to fruit peelability and hardness in citrus","volume":"13","author":"Minamikawa","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.729097","article-title":"Detection of anomalous grapevine berries using variational autoencoders","volume":"13","author":"Miranda","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giy153","article-title":"Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective","volume":"8","author":"Mochida","year":"2019","journal-title":"GigaScience"},{"key":"B75","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1186\/s12967-025-06446-x","article-title":"Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review","volume":"23","author":"Morabito","year":"2025","journal-title":"J. Transl. Med."},{"key":"B76","doi-asserted-by":"publisher","DOI":"10.1093\/g3journal\/jkac078","article-title":"Breedbase: a digital ecosystem for modern plant breeding","volume":"12","author":"Morales","year":"2022","journal-title":"G3 (Bethesda)"},{"key":"B77","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2023.1203546","article-title":"Explainable deep learning in plant phenotyping","volume":"6","author":"Mostafa","year":"2023","journal-title":"Front. Artif. Intell."},{"key":"B78","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: a state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Mu\u00f1oz-Sabater","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"B79","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1146\/annurev-arplant-070523-042828","article-title":"Deep learning in image-based plant phenotyping","volume":"75","author":"Murphy","year":"2024","journal-title":"Annu. Rev. Plant Biol."},{"key":"B80","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/s13007-019-0479-8","article-title":"Plant disease identification using explainable 3D deep learning on hyperspectral images","volume":"15","author":"Nagasubramanian","year":"2019","journal-title":"Plant Methods"},{"key":"B81","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1093\/plcell\/koac322","article-title":"Gene-by-environment interactions in plants: molecular mechanisms, environmental drivers, and adaptive plasticity","volume":"35","author":"Napier","year":"2023","journal-title":"Plant Cell"},{"key":"B82","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1434229","article-title":"Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection","volume":"15","author":"Novielli","year":"2024","journal-title":"Front. Plant Sci."},{"key":"B83","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1534\/g3.116.027524","article-title":"Genomic selection in multi-environment crop trials","volume":"6","author":"Oakey","year":"2016","journal-title":"G3 (Bethesda)"},{"key":"B84","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2012.00110","article-title":"Soybean proteome database 2012: update on the comprehensive data repository for soybean proteomics","volume":"3","author":"Ohyanagi","year":"2012","journal-title":"Front. Plant Sci."},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.3389\/fbinf.2024.1384497","article-title":"Sequencing technologies and hardware-accelerated parallel computing transform computational genomics research","volume":"4","author":"Olbrich","year":"2024","journal-title":"Front. Bioinform."},{"key":"B86","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s00425-025-04843-6","article-title":"A comparative study highlights superiority of LSTM in crop genomic prediction","volume":"262","author":"Pan","year":"2025","journal-title":"Planta"},{"key":"B87","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1016\/j.tplants.2024.07.005","article-title":"Unmasking complexities of combined stresses for creating climate-smart crops","volume":"29","author":"Pandey","year":"2024","journal-title":"Trends Plant Sci."},{"key":"B88","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1111\/nph.16544","article-title":"Enabling reusability of plant phenomic datasets with MIAPPE 1.1","volume":"227","author":"Papoutsoglou","year":"2020","journal-title":"New Phytol."},{"key":"B89","doi-asserted-by":"publisher","DOI":"10.3390\/s24051442","article-title":"Chlorophyll fluorescence imaging for environmental stress diagnosis in crops","volume":"24","author":"Park","year":"2024","journal-title":"Sensors (Basel)"},{"key":"B90","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109734","article-title":"Deep learning for plant stress detection: a comprehensive review of technologies, challenges, and future directions","volume":"229","author":"Paul","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"B91","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2020.00447","article-title":"Recommendations for choosing the genotyping method and best practices for quality control in crop genome-wide association studies","volume":"11","author":"Pavan","year":"2020","journal-title":"Front. Genet."},{"key":"B92","doi-asserted-by":"publisher","first-page":"3488","DOI":"10.1038\/s41467-024-47744-0","article-title":"Deep learning the cis-regulatory code for gene expression in selected model plants","volume":"15","author":"Peleke","year":"2024","journal-title":"Nat. Commun."},{"key":"B93","doi-asserted-by":"publisher","first-page":"D543","DOI":"10.1093\/nar\/gkae1011","article-title":"The PRIDE database at 20 years: 2025 update","volume":"53","author":"Perez-Riverol","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"B94","doi-asserted-by":"publisher","first-page":"217","DOI":"10.5194\/soil-7-217-2021","article-title":"SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty","volume":"7","author":"Poggio","year":"2021","journal-title":"Soil"},{"key":"B95","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1233794","article-title":"Pitfalls and potential of high-throughput plant phenotyping platforms","volume":"14","author":"Poorter","year":"2023","journal-title":"Front. Plant Sci."},{"key":"B96","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baaf011","article-title":"Assessing the performance of generative artificial intelligence in retrieving information against manually curated genetic and genomic data","volume":"2025","author":"Poretsky","year":"2025","journal-title":"Database (Oxford)"},{"key":"B97","doi-asserted-by":"publisher","DOI":"10.1017\/qpb.2021.18","article-title":"Plant genome sequence assembly in the era of long reads: progress, challenges and future directions","volume":"3","author":"Pucker","year":"2022","journal-title":"Quant. Plant Biol."},{"key":"B98","doi-asserted-by":"publisher","first-page":"2518","DOI":"10.1021\/acs.jproteome.4c00187","article-title":"Meta-analysis of rice phosphoproteomics data to understand variation in cell signaling across the rice pan-genome","volume":"23","author":"Ramsbottom","year":"2024","journal-title":"J. Proteome Res."},{"key":"B99","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005752","article-title":"mixOmics: an R package for 'omics feature selection and multiple data integration","volume":"13","author":"Rohart","year":"2017","journal-title":"PLoS Comput. Biol."},{"key":"B100","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.tplants.2024.03.004","article-title":"The plant disease triangle facing climate change: a molecular perspective","volume":"29","author":"Roussin-Leveillee","year":"2024","journal-title":"Trends Plant Sci"},{"key":"B101","doi-asserted-by":"publisher","DOI":"10.1093\/biomethods\/bpaf028","article-title":"Navigating the multiverse: a hitchhiker's guide to selecting harmonization methods for multimodal biomedical data","volume":"10","author":"Saras","year":"2025","journal-title":"Biol. Methods Protoc."},{"key":"B102","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.tplants.2023.08.001","article-title":"Cyber-agricultural systems for crop breeding and sustainable production","volume":"29","author":"Sarkar","year":"2024","journal-title":"Trends Plant Sci."},{"key":"B103","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1534\/genetics.117.300374","article-title":"Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize","volume":"208","author":"Schrag","year":"2018","journal-title":"Genetics"},{"key":"B104","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1603547","article-title":"PubPlant - a continuously updated online resource for sequenced and published plant genomes","volume":"16","author":"Schwacke","year":"2025","journal-title":"Front. Plant Sci."},{"key":"B105","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2021.729645","article-title":"Improving quantitative traits in self-pollinated crops using simulation-based selection with minimal crossing","volume":"12","author":"Sekine","year":"2021","journal-title":"Front. Plant Sci."},{"key":"B106","doi-asserted-by":"publisher","first-page":"D1614","DOI":"10.1093\/nar\/gkad980","article-title":"RefMetaPlant: a reference metabolome database for plants across five major phyla","volume":"52","author":"Shi","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B107","doi-asserted-by":"publisher","first-page":"3633","DOI":"10.3390\/rs70403633","article-title":"A hidden Markov models approach for crop classification: linking crop phenology to time series of multi-sensor remote sensing data","volume":"7","author":"Siachalou","year":"2015","journal-title":"Remote Sens."},{"key":"B108","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1093\/bioinformatics\/bty1054","article-title":"DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays","volume":"35","author":"Singh","year":"2019","journal-title":"Bioinformatics"},{"key":"B109","doi-asserted-by":"publisher","DOI":"10.3390\/rs12193136","article-title":"Applications of remote sensing in precision agriculture: a review","volume":"12","author":"Sishodia","year":"2020","journal-title":"Remote Sens."},{"key":"B110","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1032820","article-title":"Climate change impedes plant immunity mechanisms","volume":"13","author":"Son","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B111","doi-asserted-by":"publisher","DOI":"10.1002\/pmic.201800220","article-title":"Assessment and refinement of sample preparation methods for deep and quantitative plant proteome profiling","volume":"18","author":"Song","year":"2018","journal-title":"Proteomics"},{"key":"B112","doi-asserted-by":"publisher","DOI":"10.3390\/s24082647","article-title":"Smart sensors and smart data for precision agriculture: a review","volume":"24","author":"Soussi","year":"2024","journal-title":"Sensors (Basel)"},{"key":"B113","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01035","article-title":"nasapower: a NASA POWER global meteorology, surface solar energy and climatology data client for R","volume":"3","author":"Sparks","year":"2018","journal-title":"J. Open Source Software"},{"key":"B114","doi-asserted-by":"publisher","first-page":"8383","DOI":"10.1093\/nar\/gkad616","article-title":"JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom","volume":"51","author":"Sreedasyam","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"B115","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae593","article-title":"AutoXAI4Omics: an automated explainable AI tool for omics and tabular data","volume":"26","author":"Strudwick","year":"2025","journal-title":"Brief Bioinform."},{"key":"B116","doi-asserted-by":"publisher","first-page":"D463","DOI":"10.1093\/nar\/gkv1042","article-title":"Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools","volume":"44","author":"Sud","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"B117","doi-asserted-by":"publisher","first-page":"100979","DOI":"10.1016\/j.gendis.2023.04.018","article-title":"Pretreating and normalizing metabolomics data for statistical analysis","volume":"11","author":"Sun","year":"2024","journal-title":"Genes Dis."},{"key":"B118","doi-asserted-by":"publisher","DOI":"10.3390\/drones8060212","article-title":"Review of crop phenotyping in field plot experiments using UAV-mounted sensors and algorithms","volume":"8","author":"Tanaka","year":"2024","journal-title":"Drones"},{"key":"B119","doi-asserted-by":"publisher","first-page":"D1579","DOI":"10.1093\/nar\/gkad811","article-title":"PMhub 1.0: a comprehensive plant metabolome database","volume":"52","author":"Tian","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B120","doi-asserted-by":"publisher","DOI":"10.34133\/2019\/9237136","article-title":"How convolutional neural networks diagnose plant disease","volume":"2019","author":"Toda","year":"2019","journal-title":"Plant Phenomics"},{"key":"B121","doi-asserted-by":"publisher","DOI":"10.1002\/tpg2.70138","article-title":"Ensemble analysis with interpretable genomic prediction (EasiGP): computational tool for interpreting ensembles of genomic prediction models","volume":"18","author":"Tomura","year":"2025","journal-title":"Plant Genome"},{"key":"B122","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-026-06438-8","article-title":"Machine learning for multi-omics data integration in crop improvement: a systematic review","volume":"27","author":"Tsega","year":"2026","journal-title":"BMC Bioinf."},{"key":"B123","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1186\/s13007-025-01441-1","article-title":"Automated generation of ground truth images of greenhouse-grown plant shoots using a GAN approach","volume":"21","author":"Ullah","year":"2025","journal-title":"Plant Methods"},{"key":"B124","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1135455","article-title":"Design, execution, and interpretation of plant RNA-seq analyses","volume":"14","author":"Upton","year":"2023","journal-title":"Front. Plant Sci."},{"key":"B125","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2025.117299","article-title":"Synchrotron-based 3D X-ray computed tomography reveals root system architecture: plastic responses to phosphorus placement","volume":"457","author":"van der Bom","year":"2025","journal-title":"Geoderma"},{"key":"B126","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2020.101890","article-title":"Machine learning in plant science and plant breeding","volume":"24","author":"van Dijk","year":"2021","journal-title":"iScience"},{"key":"B129","doi-asserted-by":"publisher","first-page":"4414","DOI":"10.3168\/jds.2007-0980","article-title":"Efficient methods to compute genomic predictions","volume":"91","author":"VanRaden","year":"2008","journal-title":"J. Dairy Sci."},{"key":"B127","doi-asserted-by":"publisher","first-page":"3421","DOI":"10.1093\/plcell\/koab211","article-title":"The Arabidopsis PeptideAtlas: harnessing worldwide proteomics data to create a comprehensive community proteomics resource","volume":"33","author":"van Wijk","year":"2021","journal-title":"Plant Cell"},{"key":"B128","doi-asserted-by":"publisher","first-page":"3984","DOI":"10.1021\/acs.jproteome.4c00320","article-title":"The Zea mays PeptideAtlas: a new maize community resource","volume":"23","author":"van Wijk","year":"2024","journal-title":"J. Proteome Res."},{"key":"B130","doi-asserted-by":"publisher","DOI":"10.1093\/plphys\/kiaf132","article-title":"Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery","volume":"197","author":"Varela","year":"2025","journal-title":"Plant Physiol."},{"key":"B131","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.tplants.2005.10.004","article-title":"Genomics-assisted breeding for crop improvement","volume":"10","author":"Varshney","year":"2005","journal-title":"Trends Plant Sci."},{"key":"B132","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1038\/s41592-021-01343-9","article-title":"Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO","volume":"19","author":"Velten","year":"2022","journal-title":"Nat. Methods"},{"key":"B133","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1146\/annurev-genet-120116-024846","article-title":"On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics","volume":"52","author":"Wallace","year":"2018","journal-title":"Annu. Rev. Genet."},{"key":"B134","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0153","article-title":"Advancements in imaging sensors and AI for plant stress detection: a systematic literature review","volume":"6","author":"Walsh","year":"2024","journal-title":"Plant Phenomics"},{"key":"B136","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-022-00839-5","article-title":"Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature","volume":"18","author":"Wang","year":"2022","journal-title":"Plant Methods"},{"key":"B139","doi-asserted-by":"publisher","first-page":"6856","DOI":"10.1038\/s41467-024-50701-6","article-title":"Prediction of plant complex traits via integration of multi-omics data","volume":"15","author":"Wang","year":"2024","journal-title":"Nat. Commun."},{"key":"B140","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaa498","article-title":"NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B135","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","author":"Wang","year":"2014","journal-title":"Nat. Methods"},{"key":"B141","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.1038\/s41467-021-23774-w","article-title":"MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"B138","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2024.101223","article-title":"Cropformer: an interpretable deep learning framework for crop genomic prediction","volume":"6","author":"Wang","year":"2025","journal-title":"Plant Commun."},{"key":"B137","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf191","article-title":"WheatGP, a genomic prediction method based on CNN and LSTM","volume":"26","author":"Wang","year":"2025","journal-title":"Brief Bioinform."},{"key":"B142","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2021.699589","article-title":"Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks","volume":"12","author":"Westhues","year":"2021","journal-title":"Front. Plant Sci."},{"key":"B143","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad438","article-title":"A transformer-based genomic prediction method fused with knowledge-guided module","volume":"25","author":"Wu","year":"2024","journal-title":"Brief Bioinform."},{"key":"B145","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s00122-016-2691-5","article-title":"Envirotyping for deciphering environmental impacts on crop plants","volume":"129","author":"Xu","year":"2016","journal-title":"Theor. Appl. Genet."},{"key":"B144","doi-asserted-by":"publisher","DOI":"10.34133\/2022\/9760269","article-title":"A review of high-throughput field phenotyping systems: focusing on ground robots","volume":"2022","author":"Xu","year":"2022","journal-title":"Plant Phenomics"},{"key":"B146","doi-asserted-by":"publisher","first-page":"D1491","DOI":"10.1093\/nar\/gkab945","article-title":"qPTMplants: an integrative database of quantitative post-translational modifications in plants","volume":"50","author":"Xue","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"B147","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s43897-022-00038-9","article-title":"Recent advances in proteomics and metabolomics in plants","volume":"2","author":"Yan","year":"2022","journal-title":"Mol. Hortic."},{"key":"B148","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1186\/s12870-024-04934-8","article-title":"Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.)","volume":"24","author":"Yeon","year":"2024","journal-title":"BMC Plant Biol."},{"key":"B149","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1534\/g3.119.400154","article-title":"Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes","volume":"9","author":"Yu","year":"2019","journal-title":"G3 (Bethesda)"},{"key":"B151","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1186\/s13059-024-03401-9","article-title":"Assessing and mitigating batch effects in large-scale omics studies","volume":"25","author":"Yu","year":"2024","journal-title":"Genome Biol."},{"key":"B150","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf414","article-title":"EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models","volume":"26","author":"Yu","year":"2025","journal-title":"Brief Bioinform."},{"key":"B152","doi-asserted-by":"publisher","first-page":"D640","DOI":"10.1093\/nar\/gkad1045","article-title":"MetaboLights: open data repository for metabolomics","volume":"52","author":"Yurekten","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B153","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.tplants.2021.02.011","article-title":"Global warming, climate change, and environmental pollution: Recipe for a multifactorial stress combination disaster","volume":"26","author":"Zandalinas","year":"2021","journal-title":"Trends Plant Sci."},{"key":"B154","doi-asserted-by":"publisher","DOI":"10.1093\/hr\/uhac225","article-title":"Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa","volume":"10","author":"Zhang","year":"2023","journal-title":"Hortic. Res."},{"key":"B157","doi-asserted-by":"publisher","DOI":"10.3390\/plants13213088","article-title":"Research progress of spectral imaging techniques in plant phenotype studies","volume":"13","author":"Zhang","year":"2024","journal-title":"Plants (Basel)"},{"key":"B156","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1038\/s41467-025-57550-x","article-title":"Integration of multi-omics data accelerates molecular analysis of common wheat traits","volume":"16","author":"Zhang","year":"2025","journal-title":"Nat. Commun."},{"key":"B155","doi-asserted-by":"publisher","first-page":"0007","DOI":"10.34133\/plantphenomics.0007","article-title":"Multispectral drone imagery and SRGAN for rapid phenotypic mapping of individual Chinese cabbage plants","volume":"2022","author":"Zhang","year":"2022","journal-title":"Plant Phenomics"},{"key":"B158","doi-asserted-by":"publisher","DOI":"10.3389\/fbinf.2022.1027457","article-title":"Integration of multi-omics technologies for crop improvement: Status and prospects","volume":"2","author":"Zhang","year":"2022","journal-title":"Front. Bioinform."},{"key":"B159","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0200","article-title":"Grain protein content phenotyping in rice via hyperspectral imaging technology and a genome-wide association study","volume":"6","author":"Zheng","year":"2024","journal-title":"Plant Phenomics"},{"key":"B160","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1111\/jipb.13791","article-title":"Big data and artificial intelligence-aided crop breeding: Progress and prospects","volume":"67","author":"Zhu","year":"2025","journal-title":"J. Integr. Plant Biol."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1929693\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:06:32Z","timestamp":1787234792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1929693\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,20]]},"references-count":160,"alternative-id":["10.3389\/fpls.2026.1929693"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1929693","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,20]]},"article-number":"1929693"}}