{
  "abstract": "Accurate prediction of edible-leaf mineral composition is challenging because tissue ionomes integrate nutrient supply, plant identity, growth dilution, water-balance regulation and cumulative environmental exposure. We combined a designed nutrient-gradient experimental framework with machine learning to predict leaf ionome using 1,163 Chinese spinach (Amaranthus dubius) and Chinese broccoli (Brassica oleracea Alboglabra Group) plants grown across 16 designed nutrient-gradient experiments in a semi-controlled tropical greenhouse. Targeted perturbations of N, P, K, Ca, Mg, Fe and Mo were combined with cumulative transpiration, integrated microclimate, shoot dry weight and laboratory quantification of 12 harvested-leaf mineral targets. The resulting ionome was broad, non-Gaussian and species dependent, with coordinated off-target shifts also evident for minerals whose supplied concentrations were held constant. We benchmarked AutoGluon and TabPFN using five-fold leakage-free, distribution-balanced grouped cross-validation, in which all biological replicates from each dosing condition were withheld together. Across previously unseen nutrient conditions, pooled out-of-fold R2 ranged from 0.72 to 0.92, with broadly comparable performance between architectures. Learning curves showed rapid early gains for several targets but persistent condition-level generalization gaps for total reduced nitrogen, K, Mn and Zn. Model-agnostic Shapley additive explanations identified target-specific combinations of nutrient inputs, plant type, substrate, cumulative environment and physiological traits, while cross-model attribution agreement varied among minerals. Finally, reduced-feature models were tested on an independent grower-operated cohort of 99 plants. Calibration was closest for NO3, Mg and B, whereas Mn and Na showed substantial external bias. These results establish leakage-aware predictive ionomics as a robust framework for post-harvest lab-free crop mineral estimation while defining the calibration and sensing requirements for commercial deployment.",
  "abstract_shown": true,
  "affiliations": [
    "The Robert H Smith Faculty of Agriculture, Food and Environment, Hebrew University of Jerusalem, Israel"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "I. Shenhar"
    },
    {
      "name": "M. Pebes-Trujillo"
    },
    {
      "name": "A. Harikumar"
    },
    {
      "name": "Z. Zhao"
    },
    {
      "name": "L. Y. Tan"
    },
    {
      "name": "M. I. Setyawati"
    },
    {
      "name": "J. He"
    },
    {
      "name": "I. Herrmann"
    },
    {
      "name": "K. W. Ng"
    },
    {
      "name": "M. Gavish"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Moshelion"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/45205qwrmfnjf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.16.751971",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.751971v1.full",
  "id": "45205qwrmfnjf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.751971v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "plant biology"
    ],
    [
      "openalex",
      "1111|Plant Growth and Agriculture Techniques"
    ]
  ],
  "openalex": "W7213500550",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.751971v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/45205qwrmfnjf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:55Z",
      "native_id": "10.64898/2026.09.16.751971",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.16.751971v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:plant biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "Designed nutrient-gradient phenotyping enables predictive ionomics of tropical leafy greens",
  "title_text": "Designed nutrient-gradient phenotyping enables predictive ionomics of tropical leafy greens",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:55Z",
  "url": "https://research.draconitic.com/paper/45205qwrmfnjf",
  "venue": "bioRxiv",
  "versions": []
}