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---
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
---
# πŸ₯¬ CABBAGE: Comprehensive Agricultural Benchmark Backed by AI-Guided Evaluation
[**🌐 Homepage**](https://huggingface.co/datasets/deepplants/cabbage) | [**πŸ† Leaderboard**](https://huggingface.co/datasets/deepplants/cabbage) | [**πŸ€— Dataset**](https://huggingface.co/datasets/deepplants/cabbage) | [**πŸ’» GitHub**](https://github.com/deepplants/cabbage)
## Table of Contents
- [CABBAGE: Comprehensive Agricultural Benchmark Backed by AI-Guided Evaluation](#%F0%9F%A5%AC-cabbage-comprehensive-agricultural-benchmark-backed-by-ai-guided-evaluation)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#benchmark-tracks-and-subsets)
- [πŸ–ΌοΈ Agronomic Visual Cognition](#%F0%9F%96%BC%EF%B8%8F-agronomic-visual-cognition)
- [πŸ“š Agricultural Scientific Knowledge](#%F0%9F%93%9A-agricultural-scientific-knowledge)
- [πŸ› οΈ Agricultural Procedural Reasoning](#%F0%9F%9B%A0%EF%B8%8F-agricultural-procedural-reasoning)
- [Loading the Dataset](#loading-the-dataset)
## 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
<img src="https://cdn-uploads.huggingface.co/production/uploads/6824cf491019386a26b831c1/PYEaWom37vSKPDfKqt7fo.png" width="600"/>
### πŸ–ΌοΈ 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 β€” visual QA for pathology
### πŸ“š 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
### πŸ› οΈ 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:
```python
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")
```