--- 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**](https://huggingface.co/datasets/deepplants/cabbage) | [**🏆 Leaderboard**](https://huggingface.co/datasets/deepplants/cabbage) | [**🤗 Dataset**](https://huggingface.co/datasets/deepplants/cabbage) | [**💻 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) - [🖼️ Agronomic Visual Cognition](#%F0%9F%96%BC%EF%B8%8F-agronomic-visual-cognition) - [📚 Agricultural Scientific Knowledge](#%F0%9F%93%9A-agricultural-scientific-knowledge) - [🛠️ 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 ### 🖼️ 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: ```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") ```