[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123137-en":3,"doc-seo-123137-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123137,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning algorithms translate big data into predictive breeding accuracy - Feature Review","Statistical machine learning extracts patterns from genomic, phenotypic, and environmental datasets to build robust models for genomic-enabled prediction in plant breeding. The review shows how ML automatically selects relevant features, uses cross-validation for reliability, and analyzes genotype-by-environment (G×E) interactions. Leveraging historical breeding records, multitrait genomics, phenomics, and environmental covariables, ML streamlines analyses and improves prediction accuracy. It also discusses how data-driven strategies enhance understanding of G×E and optimize breeding decisions across diverse, extensive datasets.","Trends in  \nPlant Science  \nOPEN ACCESS  \nFeature Review  \nMachine learning algorithms translate big data into predictive breeding accuracy  \nJosé Crossa  1,2,3,4 , Osval A. Montesinos-Lopez 5 , Germano Costa-Neto 6 , Paolo Vitale 3 , Johannes W. R. Martini 7 , Daniel Runcie 8 , Roberto Fritsche-Neto 1 , Abelardo Montesinos-Lopez 9 , Paulino Pérez-Rodríguez 2 , Guillermo Gerard 3 , Susanna Dreisigacker 3 , Leonardo Crespo-Herrera 3 , Carolina Saint Pierre 3 , Morten Lillemo 10 , Jaime Cuevas 11 , Alison Bentley 12,* , and Rodomiro Ortiz 13,*  \nStatistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multitrait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets.  \nThe impact of data-driven strategies and ML techniques  \nValuation and selection processes are crucial in plant breeding for identifying desirable traits such as disease resistance, drought and heat tolerance, and high grain yield. Testing these selected cultivars across different environments through multi-environment trials (METs; see Glossary) assists in understanding their performance and stability.  \nAccurate and early predictions have a pivotal role in plant breeding [1] . With advances in statistical modeling and data analysis, breeders can now predict the performance of breeding lines or cultivars with greater precision. This allows more informed and accurate decisions and also reduces the time and resources required for developing superior cultivars. Early predictions assist breeders in focusing on the most promising genotypes, thereby accelerating the breeding cycle and enhancing the efficiency of the breeding process. Nevertheless, data collected from METs are intrinsically complex owing to structural patterns, nonstructural noise, and relationships among genotypes, environments, and genotypes and environments considered jointly, namely genotype × environment (G×E) interactions [1] . Pattern implies that cultivars respond to specific environments (location, years, location–year combinations) in a systematic and interpretable manner, whereas noise suggests that the responses are unpredictable and uninterpretable.  \nGenomic markers have revolutionized plant breeding by enabling precise selection of desirable traits at the DNA level. This accelerates the breeding process, increases accuracy in predicting plant performance, reduces costs, and enhances the development of pest/stress-resistant and high-yield cultivars, thus making plant breeding faster, more efficient, and more effective [2] .  \nHighlights  \nThe genomic prediction (GP) approach that uses genotypic and phenotypic data to predict the genomic estimated breeding value (GEBV) of individuals has been widely adopted by both public and private breeding organizations. GP models can predict the performance of plant germplasm in different environments by correctly modeling genotype × environment interactions (G×E) across multiple traits.  \nMachine learning (ML) algorithms can help breeders to determine the most effective parental selection, mating designs, population sizes, and selection intensities to maximize selection gain ata given budget while minimizing the loss of genetic ","cbCaidIaJ1UrNodb","https://ap.wps.com/l/cbCaidIaJ1UrNodb","pdf",2612148,1,18,"English","en",105,"# The impact of data-driven strategies and ML techniques\n## Valuation and selection in plant breeding\n## Genotype-by-environment (G×E) interactions\n# Highlights\n## Genomic prediction with GEBV and G×E modeling\n## ML for parental selection and breeding design\n## Neural networks for GP accuracy","[{\"question\":\"How does machine learning improve prediction in plant breeding?\",\"answer\":\"ML extracts patterns from genomic, phenotypic, and environmental data, identifies relevant features, and uses cross-validation to build robust models. It improves the reliability of predictions for new breeding lines.\"},{\"question\":\"What role do genotype-by-environment (G×E) interactions play?\",\"answer\":\"ML analyses of G×E interactions help reveal genetic factors affecting performance in specific environments. This improves understanding of how cultivars respond systematically versus unpredictably.\"},{\"question\":\"Why are early, accurate predictions important in breeding programs?\",\"answer\":\"Early predictions allow breeders to focus on the most promising genotypes, accelerating the breeding cycle. They also reduce time and resources needed to develop superior cultivars.\"}]","Machine learning algorithms translate big data into predictive breeding accuracy - Feature Review | PDF",1785814816,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-algorithms-translate-big-data-into-predictive-breeding-accuracy-feature-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-algorithms-translate-big-data-into-predictive-breeding-accuracy-feature-review/123137/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does machine learning improve prediction in plant breeding?","Question",{"text":76,"@type":77},"ML extracts patterns from genomic, phenotypic, and environmental data, identifies relevant features, and uses cross-validation to build robust models. It improves the reliability of predictions for new breeding lines.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role do genotype-by-environment (G×E) interactions play?",{"text":81,"@type":77},"ML analyses of G×E interactions help reveal genetic factors affecting performance in specific environments. This improves understanding of how cultivars respond systematically versus unpredictably.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are early, accurate predictions important in breeding programs?",{"text":85,"@type":77},"Early predictions allow breeders to focus on the most promising genotypes, accelerating the breeding cycle. 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