{
  "abstract": "Efficient yield prediction under field conditions remains a key challenge for crop breeders and agronomists. YieldLearn is an interpretable machine-learning pipeline developed to estimate yield-related traits in durum wheat (Triticum durum) using physiological indicators measured by the Hansatech Instruments Pocket PEA. The workflow integrates fluorescence parameters (Area, Fo, Fm, Fv/Fm, PI Inst.) with field-measured agronomic traits (Booting, Height, Maturity, Lodging, Yield kg/plot, and Adjusted Yield kg/ha). Five supervised learning algorithms multinomial logistic regression, random forest, support vector machine (radial kernel), k-nearest neighbors, and decision tree were evaluated using five-fold cross-validation. Models achieved average classification accuracy around 0.57 with AUC values exceeding 0.58. SHAP (SHapley Additive exPlanations) visualizations revealed that high Fv/Fm and PI Inst. values were positively associated with yield potential, whereas elevated Fo indicated stress-induced yield reduction. An interactive Shiny App enables real-time prediction and feature-importance exploration. Given the small sample size (165 plots) and absence of environmental covariates such as temperature or soil moisture, YieldLearn should be regarded as a reproducible exploratory framework rather than a production-ready predictor. Nonetheless, it offers a valuable foundation for guiding experimental design, validating physiological indicators, and supporting the next generation of scalable, interpretable crop-yield models.",
  "abstract_shown": true,
  "affiliations": [
    "National Research Council Canada"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "X. Zhang"
    },
    {
      "name": "H. Shaterian"
    },
    {
      "name": "P. Ashe"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/axrsa3x3mgcf0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.22.753232",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753232v1.full",
  "id": "axrsa3x3mgcf0",
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  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753232v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7214657343",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753232v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/axrsa3x3mgcf0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:56Z",
      "native_id": "10.64898/2026.09.22.753232",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753232v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "YieldLearn: An interpretable machine-learning pipeline for estimating yield-related traits based on physiological indicators",
  "title_text": "YieldLearn: An interpretable machine-learning pipeline for estimating yield-related traits based on physiological indicators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:56Z",
  "url": "https://research.draconitic.com/paper/axrsa3x3mgcf0",
  "venue": "bioRxiv",
  "versions": []
}