Datasets:

deepplants
/

cabbage

dataset_info:
  - config_name: agricultural_procedural_reasoning
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: options
        struct:
          - name: default
            sequence: string
          - name: diff_1
            sequence: string
          - name: diff_2
            sequence: string
          - name: diff_3
            sequence: string
          - name: diff_4
            sequence: string
          - name: diff_5
            sequence: string
      - name: answer
        dtype: string
      - name: accepted_answers
        sequence: string
      - name: category
        dtype: string
      - name: task_type
        dtype: string
      - name: question_type
        sequence: string
      - name: metadata
        dtype: string
    splits:
      - name: wikihow_arrange
        num_bytes: 658484
        num_examples: 557
      - name: wikihow_missing
        num_bytes: 604774
        num_examples: 526
      - name: wikihow_next
        num_bytes: 602898
        num_examples: 538
      - name: wikihow_all
        num_bytes: 669007
        num_examples: 520
    download_size: 1052251
    dataset_size: 2535163
  - config_name: agricultural_scientific_knowledge
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: options
        struct:
          - name: default
            sequence: string
          - name: diff_1
            dtype: 'null'
          - name: diff_2
            dtype: 'null'
          - name: diff_3
            dtype: 'null'
          - name: diff_4
            dtype: 'null'
          - name: diff_5
            dtype: 'null'
      - name: answer
        dtype: string
      - name: accepted_answers
        sequence: string
      - name: category
        dtype: string
      - name: task_type
        dtype: string
      - name: question_type
        sequence: string
      - name: metadata
        dtype: string
    splits:
      - name: agriexam
        num_bytes: 1816672
        num_examples: 4548
      - name: cca_ceu
        num_bytes: 345105
        num_examples: 689
      - name: embrapa
        num_bytes: 32339083
        num_examples: 19682
    download_size: 14476971
    dataset_size: 34500860
  - config_name: agronomic_visual_cognition
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: images
        sequence: image
      - name: options
        struct:
          - name: default
            sequence: string
          - name: diff_1
            sequence: string
          - name: diff_2
            sequence: string
          - name: diff_3
            sequence: string
          - name: diff_4
            sequence: string
          - name: diff_5
            sequence: string
      - name: answer
        dtype: string
      - name: accepted_answers
        sequence: string
      - name: category
        dtype: string
      - name: task_type
        dtype: string
      - name: question_type
        sequence: string
      - name: metadata
        dtype: string
    splits:
      - name: eppo
        num_bytes: 938281149.36
        num_examples: 26428
      - name: plantnet
        num_bytes: 2848270999.5
        num_examples: 20350
      - name: bppq
        num_bytes: 14213980
        num_examples: 368
    download_size: 3720715335
    dataset_size: 3800766128.86
configs:
  - config_name: agricultural_procedural_reasoning
    data_files:
      - split: wikihow_arrange
        path: agricultural_procedural_reasoning/wikihow_arrange-*
      - split: wikihow_missing
        path: agricultural_procedural_reasoning/wikihow_missing-*
      - split: wikihow_next
        path: agricultural_procedural_reasoning/wikihow_next-*
      - split: wikihow_all
        path: agricultural_procedural_reasoning/wikihow_all-*
  - config_name: agricultural_scientific_knowledge
    data_files:
      - split: agriexam
        path: agricultural_scientific_knowledge/agriexam-*
      - split: cca_ceu
        path: agricultural_scientific_knowledge/cca_ceu-*
      - split: embrapa
        path: agricultural_scientific_knowledge/embrapa-*
  - config_name: agronomic_visual_cognition
    default: true
    data_files:
      - split: eppo
        path: agronomic_visual_cognition/eppo-*
      - split: plantnet
        path: agronomic_visual_cognition/plantnet-*
      - split: bppq
        path: agronomic_visual_cognition/bppq-*
license: cc-by-nc-nd-4.0
task_categories:
  - visual-question-answering
  - question-answering
  - zero-shot-image-classification
  - multiple-choice
language:
  - en
pretty_name: CABBAGE
tags:
  - biology
  - agriculture

🥬 CABBAGE: Comprehensive Agricultural Benchmark Backed by AI-Guided Evaluation

🌐 Homepage | 🏆 Leaderboard | 🤗 Dataset | 💻 GitHub

CABBAGE: Comprehensive Agricultural Benchmark Backed by AI-Guided Evaluation
- Table of Contents
- Dataset Description

Dataset Description

Homepage: https://huggingface.co/datasets/deepplants/cabbage
Repository: https://github.com/deepplants/cabbage
Paper: Not yet published
Leaderboard: https://huggingface.co/datasets/deepplants/cabbage
Size of downloaded dataset files: 3.74 GB
Size of the auto-converted Parquet files: 240.84 MB
Number of rows: 74,206

CABBAGE is a large-scale, multimodal benchmark for evaluating AI systems in agriculture across three complementary task macro-categories: Visual Cognition, Scientific Knowledge, and Procedural Reasoning. Each macro-category contains high-quality, domain-specific subsets built from curated or expert-reviewed sources.

Benchmark Tracks and Subsets

🖼️ Agronomic Visual Cognition

Evaluates image-based plant understanding and visual QA.

eppo: Plant pest and disease image classification (from EPPO data)
plantnet: Species-level classification using Pl@ntNet imagery
bppq: The Big Plant Pathology Quiz — visual QA for pathology

📚 Agricultural Scientific Knowledge

Tests scientific factual knowledge, retrieval, and reasoning over structured agronomic data.

agriexam: Multiple-choice exams from official agricultural education materials
cca_ceu: Multiple-choice questions from the Certified Crop Adviser datasets and Continuing Education Unit materials
embrapa: Questions derived from Brazilian Agricultural Research Corporation technical guide series

🛠️ Agricultural Procedural Reasoning

Challenges models on procedural tasks derived from domain-relevant manuals and wikiHow entries.

wikihow_arrange: Arrange steps of an agricultural procedure in the correct order
wikihow_missing: Identify missing steps in an agricultural workflow
wikihow_next: Predict the next step in a given task
wikihow_all: Generate the full sequence of steps required to carry out an agricultural task

Loading the Dataset

You can load any specific configuration and split using the Hugging Face datasets library:

from datasets import load_dataset

# Example: Load all the splits from the Agronomic Visual Cognition subset
dataset_dict = load_dataset("deepplants/cabbage", name="agronomic_visual_cognition")

# Example: Load the Embrapa split from the Agricultural Scientific Knowledge subset
dataset = load_dataset("deepplants/cabbage", name="agricultural_scientific_knowledge", split="embrapa")