{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360021392642745472.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.3390/agriculture10100436"}},{"identifier":{"@type":"URI","@value":"https://www.mdpi.com/2077-0472/10/10/436/pdf"}}],"dc:title":[{"@value":"Machine Learning for Plant Breeding and Biotechnology"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>Classical univariate and multivariate statistics are the most common methods used for data analysis in plant breeding and biotechnology studies. Evaluation of genetic diversity, classification of plant genotypes, analysis of yield components, yield stability analysis, assessment of biotic and abiotic stresses, prediction of parental combinations in hybrid breeding programs, and analysis of in vitro-based biotechnological experiments are mainly performed by classical statistical methods. Despite successful applications, these classical statistical methods have low efficiency in analyzing data obtained from plant studies, as the genotype, environment, and their interaction (G × E) result in nondeterministic and nonlinear nature of plant characteristics. Large-scale data flow, including phenomics, metabolomics, genomics, and big data, must be analyzed for efficient interpretation of results affected by G × E. Nonlinear nonparametric machine learning techniques are more efficient than classical statistical models in handling large amounts of complex and nondeterministic information with “multiple-independent variables versus multiple-dependent variables” nature. Neural networks, partial least square regression, random forest, and support vector machines are some of the most fascinating machine learning models that have been widely applied to analyze nonlinear and complex data in both classical plant breeding and in vitro-based biotechnological studies. High interpretive power of machine learning algorithms has made them popular in the analysis of plant complex multifactorial characteristics. The classification of different plant genotypes with morphological and molecular markers, modeling and predicting important quantitative characteristics of plants, the interpretation of complex and nonlinear relationships of plant characteristics, and predicting and optimizing of in vitro breeding methods are the examples of applications of machine learning in conventional plant breeding and in vitro-based biotechnological studies. Precision agriculture is possible through accurate measurement of plant characteristics using imaging techniques and then efficient analysis of reliable extracted data using machine learning algorithms. Perfect interpretation of high-throughput phenotyping data is applicable through coupled machine learning-image processing. Some applied and potentially applicable capabilities of machine learning techniques in conventional and in vitro-based plant breeding studies have been discussed in this overview. Discussions are of great value for future studies and could inspire researchers to apply machine learning in new layers of plant breeding.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380021392642745472","@type":"Researcher","foaf:name":[{"@value":"Mohsen Niazian"}],"jpcoar:affiliationName":[{"@value":"Field and Horticultural Crops Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Jam-e Jam cross way, P.O. Box 741, 66169-36311 Sanandaj, Iran"}]},{"@id":"https://cir.nii.ac.jp/crid/1380021392642745473","@type":"Researcher","foaf:name":[{"@value":"Gniewko Niedbała"}],"jpcoar:affiliationName":[{"@value":"Department of Biosystems Engineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"20770472"}],"prism:publicationName":[{"@value":"Agriculture"}],"dc:publisher":[{"@value":"MDPI AG"}],"prism:publicationDate":"2020-09-27","prism:volume":"10","prism:number":"10","prism:startingPage":"436"},"reviewed":"false","dc:rights":["https://creativecommons.org/licenses/by/4.0/"],"url":[{"@id":"https://www.mdpi.com/2077-0472/10/10/436/pdf"}],"createdAt":"2020-09-27","modifiedAt":"2025-10-11","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360302865730396416","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Challenges in developing cell culture media using machine learning"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.3390/agriculture10100436"},{"@type":"CROSSREF","@value":"10.1016/j.biotechadv.2023.108293_references_DOI_P9TpNo4xtuUoaAUkza8r8mHk7Xi"}]}