README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nc-4.0
task_categories:
- image-text-to-text
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: images
    list:
      image:
        decode: true
  - name: id
    dtype: string
  - name: messages
    list:
    - name: role
      dtype: string
    - name: content
      list:
      - name: type
        dtype: string
      - name: text
        dtype: string
  - name: origin_dataset
    dtype: string
  - name: raw_metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 20642731765
    num_examples: 28482
  download_size: 20638215440
  dataset_size: 20642731765
---

# AgroMind

AgroMind is an agricultural remote sensing benchmark for evaluating large multimodal models on agricultural scene understanding. It contains **28,482 question-answer pairs** paired with **20,850 images** drawn from nine public datasets plus proprietary global parcel data, spanning **13 task types** across **4 evaluation dimensions**:

- **Spatial Perception** — localization, relationship determination, boundary detection
- **Object Understanding** — classification, pest/disease diagnostics, growth status assessment
- **Scene Understanding** — comparison, counting, area statistics
- **Scene Reasoning** — visual prompt reasoning, anomaly detection, climate classification, yield prediction

This dataset has been standardized to the HF `image_text_to_text` format and converted to **Parquet** with image bytes embedded directly in the dataset. All subsets (`Agriculture`, `CropHarvest`, `Fruit`, `Leaf_diseases`, `Oil_palm_trees`, `Pest`, `Rural`, `Trees`, `corn`, `crop`) have been concatenated into a single flat dataset — use the `origin_dataset` column or the `id` field (`agromind_{subset}_{index}`) to filter by source subset. Images are organized by subset under `images/{subset}/` within each row's `raw_metadata`.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

---

## Schema

Every record shares the same columns so heterogeneous subsets concatenate cleanly.

| Column | Type | Description |
|---|---|---|
| `id` | `string` | Unique row identifier of the form `agromind_{subset}_{index}` |
| `images` | `list[Image]` | List of images embedded as bytes, aligned to `{"type": "image"}` placeholders in `messages` |
| `messages` | `list[dict]` | Conversational VLM format with `role` and `content` fields |
| `origin_dataset` | `string` | Source dataset name for provenance tracking |
| `raw_metadata` | `string` | JSON-encoded string of all original source fields not folded into `messages`; always includes `file_names` pointing to `images/{subset}/filename` |

### `messages` structure

```python
[
  {
    "role": "user",
    "content": [
      {"type": "image", "text": None},   # placeholder aligned to images list
      {"type": "text", "text": "<question>"}
    ]
  },
  {
    "role": "assistant",
    "content": [{"type": "text", "text": "<answer>"}]
  }
]
```

### `raw_metadata` structure

`raw_metadata` is a JSON string (not a native struct) — this is intentional, as it allows `concatenate_datasets()` to work across subsets whose original source fields differ in type. Restore fields with `json.loads(row["raw_metadata"])`. It always contains `file_names` for image path traceability:

```python
{
  "file_names": ["images/Agriculture/filename.jpg"],
  # ...original source fields preserved verbatim
}
```

---

## Usage

```python
from datasets import load_dataset

# Load the full dataset
ds = load_dataset("Project-AgML/AgroMind")

# Stream without downloading
ds = load_dataset("Project-AgML/AgroMind", streaming=True)

# Filter by subset using origin_dataset
agriculture = ds["train"].filter(lambda x: x["origin_dataset"] == "Agriculture")

# Or filter using the id field (format: agromind_{subset}_{index})
crop_harvest = ds["train"].filter(lambda x: x["id"].split("_")[1] == "CropHarvest")
```

### Accessing images

Images are stored as embedded bytes and decoded to PIL automatically:

```python
row = ds["train"][0]
image = row["images"][0]          # PIL Image, ready to use
print(image.size)                 # (width, height)
print(image.mode)                 # RGB
```

### Restoring raw metadata

```python
import json

meta = json.loads(row["raw_metadata"])
print(meta["file_names"])         # ['images/Agriculture/filename.jpg']
```

---

## Citation

```bibtex
@misc{li2025largemultimodalmodelsunderstand,
      title={Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind},
      author={Li, Qingmei and Zhang, Yang and Mai, Zurong and Chen, Yuhang and Lou, Shuohong and Huang, Henglian and Zhang, Jiarui and Zhang, Zhiwei and Wen, Yibin and Li, Weijia and Fu, Haohuan and Huang, Jianxi and Zheng, Juepeng},
      year={2025},
      eprint={2505.12207},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.12207}
}
```