{"authors":[{"id":null,"fullName":"Wang, Yongjing","name":"Yongjing","surname":"Wang","rank":1,"pid":null},{"id":"orcid_______::1b2c851fb80b3a686ff3ede6c5a63797","fullName":"Mouazen, Abdul","name":"Abdul","surname":"Mouazen","rank":2,"pid":{"id":{"scheme":"orcid","value":"0000-0002-0354-0067"},"provenance":null}}],"openAccessColor":null,"publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"eng","label":"English"},"countries":null,"subjects":[{"subject":{"scheme":"keyword","value":"Domain adaptation"},"provenance":null},{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"keyword","value":"NIR spectroscopy"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Autoencoders"},"provenance":null},{"subject":{"scheme":"keyword","value":"Clay perdition"},"provenance":null},{"subject":{"scheme":"keyword","value":"Soil spectral libraries"},"provenance":null},{"subject":{"scheme":"keyword","value":"soil"},"provenance":null}],"mainTitle":"Domain-Invariant autoencoders and subset selection for Site-Specific clay prediction with small scale local data","subTitle":null,"descriptions":["The application of large soil spectral libraries (SSLs) for localized clay prediction is limited by domain shifts arising from soil moisture variability and instrumental differences. The study proposes a novel modelling and resampling framework for the prediction of clay content using a large SSL with dry samples and a local spectrallibrary with wet samples. The core of the framework incorporated a domain-invariant autoencoder (DAE) with a maximum mean discrepancy (MMD) regularization to align spectral features between the dry-condition SSL and the wet-condition local dataset. A standard resampling method was then applied to the transformed SSLs to selectan optimal subset of SSLs for site-specific calibration of clay content. Finally, partial least squares regression (PLSR) models were developed and validated using both a large local sample set and two subsets chosen via the standard resampling method (non-DAE-resampling) and proposed DAE-resampling methods. The performance of two spectrometers, namely, a AvaSpec-NIR (1000–2500 nm range) and a CompactSpec (305–1700 nm range) were evaluated. Results for the AvaSpec-NIR showed the DAE-resampling method achieved a prediction R2 value of 0.74 with its corresponding PLSR model, comparable to the PLSR model trained on the large local calibration dataset (R2 = 0.78), both significantly exceeding the non-DAE-resampling method (R2 = 0.53). For the CompactSpec spectrometer, despite covered less clay-sensitive bands, the DAE-resampling method (R2 = 0.63) still outperformed the non-DAE-resampling method (R2 = 0.48) through enhanced cross-domain bias reduction. The DAE-resampling method also reduced the subset sampling computation time by over 50 % (AvaSpec-NIR: 30 s vs. 68 s; CompactSpec: 37 s vs. 86 s). While the DAE-resampling framework demonstrates potential for cost-effective soil clay content prediction with minimal local data, its performance remains sensitive to the NIR spectral range. Future work should explore alternative architectures and extend the framework to additional soil attributes. This study advances domain adaptation strategies in spectral analytics, offering a pathway to scalable soil monitoring in data-scarce situations."],"publicationDate":"2026-02-01","publisher":"Elsevier BV","embargoEndDate":null,"sources":["Crossref"],"formats":null,"contributors":null,"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":{"name":"Computers and Electronics in Agriculture","issnPrinted":"0168-1699","issnOnline":null,"issnLinking":null,"ep":null,"iss":null,"sp":"111354","vol":"242","edition":null,"conferencePlace":null,"conferenceDate":null},"documentationUrls":null,"codeRepositoryUrl":null,"programmingLanguage":null,"contactPeople":null,"contactGroups":null,"tools":null,"size":null,"version":null,"geoLocations":null,"id":"doi_dedup___::55735e5249aed3d9a3e2bef976bd1550","originalIds":["S0168169925014607","10.1016/j.compag.2025.111354","50|doiboost____|55735e5249aed3d9a3e2bef976bd1550","50|dblp________::616ed6daa0b0009122bb1c1db6b7009a","50|od______2659::55735e5249aed3d9a3e2bef976bd1550","oai:zenodo.org:18085246"],"pids":[{"scheme":"doi","value":"10.1016/j.compag.2025.111354"}],"dateOfCollection":null,"lastUpdateTimeStamp":null,"indicators":{"citationImpact":{"citationCount":0.0,"influence":2.2251732E-9,"popularity":2.7165745E-9,"impulse":0.0,"citationClass":"C5","influenceClass":"C5","impulseClass":"C5","popularityClass":"C5"}},"projects":[{"id":"corda_____he::4cfee24e8edda5a2d82a9c5026a200d1","code":"101156480","acronym":"WHEATWATCHER","title":"Safe Wheat Agriculture Towards Sustainable Health: Innovative Sensing Techniques, and Holistic Spectroscopy Traceability for Improved Soil, plant Health and safe wheat grain","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/101156480"}]}],"organizations":[{"legalName":"Ghent University","acronym":"UGent","id":"openorgs____::5f31346d444a7f06a28c880fb170b0f6","pids":[{"scheme":"ROR","value":"https://ror.org/00cv9y106"},{"scheme":"wikidata","value":"Q1137665"},{"scheme":"FundRef","value":"501100004385"},{"scheme":"ISNI","value":"0000000120697798"},{"scheme":"fundref","value":"501100004385"},{"scheme":"FundRef","value":"501100007229"},{"scheme":"mag_id","value":"32597200"},{"scheme":"Wikidata","value":"Q1137665"},{"scheme":"fundref","value":"501100007229"},{"scheme":"OrgReg","value":"BE0060"},{"scheme":"PIC","value":"999986096"},{"scheme":"OrgRef","value":"268040"},{"scheme":"GRID","value":"grid.5342.0"}]}],"communities":[{"code":"eosc","label":"EOSC","provenance":null}],"collectedFrom":[{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},{"key":"openaire____::081b82f96300b6a6e3d282bad31cb6e2","value":"Crossref"},{"key":"openaire____::d8b68cc0a53121f6f896883a7d60c1db","value":"DBLP"}],"instances":[{"pids":[{"scheme":"doi","value":"10.1016/j.compag.2025.111354"}],"license":"Elsevier TDM","accessRight":{"code":"c_14cb","label":"CLOSED","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/","openAccessRoute":null},"type":"Article","urls":["https://doi.org/10.1016/j.compag.2025.111354"],"publicationDate":"2026-02-01","refereed":"peerReviewed","hostedBy":{"key":"issn___print::c0903cbd966b868dd11f64c1c0ba5921","value":"Computers and Electronics in Agriculture"},"collectedFrom":{"key":"openaire____::081b82f96300b6a6e3d282bad31cb6e2","value":"Crossref"}},{"alternateIdentifiers":[{"scheme":"doi","value":"10.1016/j.compag.2025.111354"}],"type":"Article","urls":["https://doi.org/10.1016/j.compag.2025.111354"],"publicationDate":"2026-01-01","refereed":"nonPeerReviewed","hostedBy":{"key":"openaire____::d8b68cc0a53121f6f896883a7d60c1db","value":"DBLP"},"collectedFrom":{"key":"openaire____::d8b68cc0a53121f6f896883a7d60c1db","value":"DBLP"}},{"alternateIdentifiers":[{"scheme":"doi","value":"10.1016/j.compag.2025.111354"},{"scheme":"oai","value":"oai:zenodo.org:18085246"}],"license":"CC BY","accessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/","openAccessRoute":null},"type":"Article","urls":["https://zenodo.org/records/18085246","http://dx.doi.org/10.1016/j.compag.2025.111354"],"publicationDate":"2025-12-29","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"}}],"links":[{"header":{"relationType":"resultProject","relationClass":"isProducedBy","relatedIdentifier":"corda_____he::4cfee24e8edda5a2d82a9c5026a200d1","relatedRecordType":"project","relationProvenance":"sysimport:crosswalk:repository","trust":"0.9"},"collectedfrom":[{"dsId":"openaire____::3f264f93cf3b0cfc4ede188a6300455c","dsName":"CORDA - 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