{
  "abstract": "Accurate analysis of heterogeneous soil records can support data-driven agricultural planning, but integrating datasets from different laboratories and public repositories introduces challenges related to inconsistent units, source-dependent distributions, missing values, duplicate observations, and potential information leakage. This study proposes ADWTabNet, an architecture-diverse TabNet ensemble for multiclass crop-type classification using harmonized multi-source soil data. The integrated dataset contained 18,237 observations representing seven crop classes and a broad set of chemical, physical, microbiological, and categorical soil properties. The proposed framework combines unit standardization, duplicate control, source-distribution harmonization, separate categorical and numerical imputation, tree-structured Parzen estimator-based hyperparameter optimization, structurally diverse TabNet models, probability-level ensemble fusion, calibration assessment, and attention-based interpretation. Under the evaluated stratified holdout protocol, XGBoost achieved a test accuracy of 0.8724 and a macro F1-score of 0.8670. Baseline TabNet improved these values to 0.8863 and 0.8822, respectively, while the TPE-optimized TabNet reached an accuracy of 0.8951 and reduced log loss from 0.3325 to 0.2566. The proposed ADWTabNet ensemble obtained the best reported performance, with a test accuracy of 0.9053, macro F1-score of 0.9025, weighted F1-score of 0.9050, macro ROC–AUC of 0.9930, and normalized multiclass Brier score of 0.0193. Repeated-run evaluation confirmed a stable advantage over the optimized individual TabNet, whereas the difference between validation-weighted and equal architecture-diverse averaging was not statistically significant. Aggregated TabNet attention ranked humus, phosphorus, electrical conductivity, nitrogen, and pH among the leading predictors, and this ranking was independently supported by permutation-importance analysis. These findings demonstrate improved crop-type classification within the evaluated integrated multi-source dataset. However, external validation using previously unseen laboratories and geographic regions is required before broader transferability can be established.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "name": "Abror Shavkatovich Buriboev"
    },
    {
      "name": "Akhatov Abror"
    },
    {
      "name": "Mamatov Narzullo"
    },
    {
      "name": "Nazarov Fayzullo"
    },
    {
      "name": "Yuldoshev Yusuf"
    },
    {
      "name": "Shavkat Buriboyev"
    },
    {
      "name": "Muratali Bazarbaev"
    },
    {
      "name": "Abbos Abduvaytov"
    },
    {
      "name": "Gayrat Achilov"
    },
    {
      "name": "Jurabek Kuvandikov"
    },
    {
      "name": "Ravshan Indiaminov"
    },
    {
      "name": "Heung Seok Jeon"
    },
    {
      "name": "Cheolwon Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fxfjbqyr51ej1.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-75366-7",
  "fulltext_url": null,
  "id": "fxfjbqyr51ej1",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-75366-7",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7222893707",
  "pdf_url": "https://www.nature.com/articles/s41598-026-75366-7_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/fxfjbqyr51ej1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1038/s41598-026-75366-7",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-75366-7"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data",
  "title_text": "Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/fxfjbqyr51ej1",
  "venue": "Scientific Reports",
  "versions": []
}