{
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  "@type": "sc:Dataset",
  "@id": "https://huggingface.co/datasets/amonshano/anonymousnips26",
  "name": "Agro",
  "alternateName": [
    "Agro",
    "Agro-OCD Benchmark"
  ],
  "description": "Agro is a benchmark for evaluating on-the-fly category discovery in agricultural open-world environments. It contains agricultural visual data spanning fungi, crop pests and diseases, and insects, and supports evaluation under cluttered field imagery, long-tailed category distributions, and hierarchical biological taxonomy.",
  "url": "https://huggingface.co/datasets/amonshano/anonymousnips26",
  "license": "https://spdx.org/licenses/Apache-2.0.html",
  "keywords": [
    "agriculture",
    "open-world recognition",
    "on-the-fly category discovery",
    "category discovery",
    "long-tailed recognition",
    "taxonomy-aware discovery",
    "image classification"
  ],
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    "@type": "Organization",
    "name": "Anonymous"
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  "citeAs": "Anonymous. (2026). Agro: A Benchmark for Evaluating On-the-Fly Category Discovery in Agricultural Open Worlds.",
  "datePublished": "2026-01-01",
  "version": "1.0.0",
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      "sha256": "https://github.com/mlcommons/croissant/issues/80"
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  ],
  "recordSet": [
    {
      "@type": "cr:RecordSet",
      "@id": "default_splits",
      "name": "default_splits",
      "description": "Splits for the default configuration.",
      "dataType": "cr:Split",
      "key": {
        "@id": "default_splits/split_name"
      },
      "field": [
        {
          "@type": "cr:Field",
          "@id": "default_splits/split_name",
          "name": "split_name",
          "description": "Name of the dataset split.",
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          "value": "train"
        }
      ],
      "data": [
        {
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      ]
    },
    {
      "@type": "cr:RecordSet",
      "@id": "default",
      "name": "default",
      "description": "Default record set for Agro image records.",
      "field": [
        {
          "@type": "cr:Field",
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          "name": "split",
          "description": "Dataset split name.",
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        {
          "@type": "cr:Field",
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          "name": "image",
          "description": "Agricultural image used for category discovery evaluation.",
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          }
        },
        {
          "@type": "cr:Field",
          "@id": "default/label",
          "name": "label",
          "description": "Category label for the image. Depending on the subset, this may correspond to a fungi category, crop pest or disease category, or biological taxonomy label.",
          "dataType": "sc:Integer",
          "source": {
            "fileSet": {
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  ],
  "rai:dataLimitations": "Agro is intended as an evaluation benchmark for on-the-fly category discovery under agricultural open-world conditions. It does not fully model long-term temporal dynamics, geographic distribution shifts, sensor variation, expert-in-the-loop diagnosis, treatment recommendation, or downstream agronomic intervention. Therefore, it should not be used as a complete substitute for field deployment validation.",
  "rai:dataBiases": "Agro may reflect biases from field-collected imagery and curated public sources, including geographic coverage bias, species and crop prevalence bias, taxonomic imbalance, visual acquisition bias, seasonal bias, sensor bias, and long-tailed category distributions. Some rare pests, diseases, fungi, or insect taxa may be underrepresented, and the dataset may not cover all agricultural regions, seasons, crop varieties, sensors, or growth stages.",
  "rai:personalSensitiveInformation": "The dataset is composed of agricultural visual imagery focused on fungi, crop pests and diseases, and insects. It is not intended to contain personal or sensitive information. Images were filtered to focus on agricultural and biological subjects; however, users should perform additional checks before downstream redistribution, deployment, or integration into applications.",
  "rai:dataUseCases": [
    "Evaluating on-the-fly category discovery algorithms in agricultural open-world settings.",
    "Benchmarking old-category recognition and novel-category discovery under streaming evaluation protocols.",
    "Auditing category over-discovery, category calibration, and taxonomy-aware consistency.",
    "Studying robustness under cluttered field imagery, long-tailed agricultural distributions, and hierarchical biological taxonomy."
  ],
  "rai:dataSocialImpact": "Agro may support research on more reliable agricultural visual recognition systems, especially for discovering rare pests, diseases, fungi, and insect taxa under realistic open-world conditions. Potential benefits include improved benchmarking for agricultural AI and better understanding of model limitations. However, models evaluated on Agro should not be directly used for high-stakes agricultural diagnosis, pest management, treatment recommendation, ecological monitoring, or treatment planning without expert validation and additional field testing.",
  "rai:hasSyntheticData": false
}