README.md

---
license: cc-by-4.0
task_categories:
  - visual-question-answering
  - image-classification
language:
  - en
tags:
  - plant-disease
  - plant-pathology
  - agriculture
  - multi-turn-vqa
  - chain-of-inquiry
  - multimodal
  - benchmark
  - medical-imaging
  - biology
  - reasoning
pretty_name: PlantInquiryVQA — Thinking Like a Botanist
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: test
        path: data/test.csv
dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: crop
      dtype: string
    - name: disease
      dtype: string
    - name: category
      dtype:
        class_label:
          names:
            - disease
            - healthy
            - insect_damage
            - senescence
    - name: severity
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: question_category
      dtype: string
    - name: visual_grounding
      dtype: string
    - name: question_number
      dtype: float64
    - name: dataset_source
      dtype: string
  splits:
    - name: train
      num_examples: 82800
    - name: test
      num_examples: 55268
  download_size: 3700000000
  dataset_size: 3700000000
---

# PlantInquiryVQA — Thinking Like a Botanist

**Benchmark and framework for multi-turn, intent-driven visual question answering in plant pathology.**

> *Accepted at **ACL 2026 Findings**.*

[![Images](https://img.shields.io/badge/images-24%2C950-green)](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA)
[![QA Pairs](https://img.shields.io/badge/QA%20pairs-138%2C068-blue)](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA)
[![License Code](https://img.shields.io/badge/code-MIT-lightgrey)](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA)
[![License Data](https://img.shields.io/badge/data-CC%20BY%204.0-orange)](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA)
[![ACL 2026](https://img.shields.io/badge/ACL-2026%20Findings-red)](https://aclanthology.org/)

---

## Overview

PlantInquiryVQA formalises diagnostic reasoning in plant pathology as a **Chain-of-Inquiry (CoI)** — an ordered sequence of visually-grounded questions that adapts to the plant's severity and the expert's epistemic intent (**Diagnosis / Prognosis / Management**).

The benchmark evaluates whether modern Multimodal Large Language Models (MLLMs) can *reason like a botanist*, not just classify a leaf. Key findings from benchmarking 18 state-of-the-art models:

- All 18 MLLMs describe symptoms competently but **fail at reliable clinical reasoning** (top Clinical Utility score = 0.188 / 1.0)
- Structured question-guided inquiry improves diagnostic accuracy by **~48%** over direct diagnosis
- Structured CoI reduces hallucination significantly compared to free-form dialogue

---

## Dataset at a Glance

| Attribute | Value |
|---|---|
| **Leaf images** | 24,950 |
| **QA pairs** | 138,068 |
| **Train / Test split** | 82,800 / 55,268 |
| **Crop species** | 34 |
| **Disease categories** | 116 |
| **Image categories** | disease · healthy · insect\_damage · senescence |
| **Severity levels** | MILD · MODERATE · SEVERE |
| **Source datasets** | 39 component datasets (see paper Appendix A.1) |
| **Image corpus size** | ~3.5 GB |

### Covered crop species (34 total)
Apple · Arabian Jasmine · Bitter Gourd · Blueberry · Bottle Gourd · Cauliflower · Cherry · Corn · Cotton · Cucumber · Eggplant/Brinjal · Grape · Guava · Hibiscus · Jackfruit · Lemon · Litchi · Mango · Orange · Papaya · Peach · Peas · Pepper · Pepper Bell · Potato · Raspberry · Rice · Rubber · Soybean · Squash · Strawberry · Sunflower · Tea · Tomato

---

## Quick Load

```python
from datasets import load_dataset

# Load train / test splits (metadata only — no images)
ds = load_dataset("SyedNazmusSakib/PlantInquiryVQA")
train = ds["train"]
test  = ds["test"]

print(train[0])
# {
#   'image_id': 'f650d82227e534b8.jpg',
#   'crop': 'Bottle Gourd',
#   'disease': 'healthy',
#   'category': 'healthy',
#   'severity': '',
#   'question': 'What crop is shown in this image?',
#   'answer': 'This leaf is from a Bottle Gourd plant ...',
#   'question_category': 'crop_identification',
#   'visual_grounding': '',
#   'question_number': 1.0,
#   'dataset_source': 'non_disease'
# }
```

### Load with images

Images live in the `images/` folder of this repository, named by `image_id`.

```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

ds = load_dataset("SyedNazmusSakib/PlantInquiryVQA", split="test")

def attach_image(row):
    img_path = hf_hub_download(
        repo_id="SyedNazmusSakib/PlantInquiryVQA",
        repo_type="dataset",
        filename=f"images/{row['image_id']}",
    )
    row["image"] = Image.open(img_path).convert("RGB")
    return row

# Attach images on demand (lazy)
sample = attach_image(ds[0])
```

### Download the full image corpus locally

```bash
# Install helper
pip install huggingface_hub

# Download everything to ./images/
python -c "
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id='SyedNazmusSakib/PlantInquiryVQA',
    repo_type='dataset',
    local_dir='./PlantInquiryVQA',
    allow_patterns=['images/*', 'data/*.csv', 'visual_cues/*', 'diseases_knowledge_base/*'],
)
"
```

Or use the provided script (after cloning the GitHub repo):

```bash
python scripts/download_images.py
```

---

## Repository Structure

```
SyedNazmusSakib/PlantInquiryVQA (HuggingFace)
├── README.md                         ← this file (dataset card)
├── CITATION.cff                      ← machine-readable citation
├── requirements.txt
│
├── data/
│   ├── train.csv                     ← 82,800 QA rows  (80% split)
│   └── test.csv                      ← 55,268 QA rows  (20% split)
│
├── images/                           ← 24,950 leaf JPEGs (~3.5 GB)
│   ├── 00009faac7cf68de.jpg
│   └── ...
│
├── diseases_knowledge_base/
│   ├── all_cards.jsonl               ← 116-disease expert knowledge cards
│   └── <crop>/<disease>.json
│
└── visual_cues/
    └── visual_cues.json              ← 24,950 × expert-verified visual cues
```

### CSV Schema

| Column | Type | Description |
|---|---|---|
| `image_id` | string | Filename of the leaf image (key into `images/`) |
| `crop` | string | Crop species (34 classes) |
| `disease` | string | Disease name or `"healthy"` |
| `category` | enum | `disease` · `healthy` · `insect_damage` · `senescence` |
| `severity` | enum | `MILD` · `MODERATE` · `SEVERE` · *(empty for healthy)* |
| `question` | string | The CoI question posed to the model |
| `answer` | string | Ground-truth expert answer |
| `question_category` | string | Semantic category of the question |
| `visual_grounding` | string | Expert-verified visual cues referenced |
| `question_number` | float | Position in the CoI chain (1 = first) |
| `dataset_source` | string | `disease_only` or `non_disease` |

---

## Evaluation Protocols

| Protocol | History given to model | Purpose |
|---|---|---|
| **Guided** (Test 1, main) | Ground-truth answers | Upper bound — isolates per-turn reasoning |
| **Scaffolded** (Test 2) | None | Lower bound — raw single-turn capability |
| **Cascading** (ablation) | Model's own prior answers | Realistic deployment — compounding errors |
| **Unconstrained** (ablation) | No CoI templates | Worst case — free-form dialogue |

---

## Benchmark Results (18 MLLMs)

Best-in-class per metric (full table in paper Table 2):

| Metric | Leader | Score |
|---|---|---:|
| Disease Accuracy (S_dis) | Gemini-3-Flash | 0.444 |
| Clinical Utility (S_clin) | Llama-3.2-90B-Vision | 0.185 |
| Safety Score (S_safe) | Llama-3.2-90B-Vision | 0.214 |
| Visual Grounding (S_vg) | Qwen-VL-Plus | 0.508 |
| Explainability Efficiency (E) | Grok-4.1-Fast | 5.20 |

Models benchmarked include: Gemini 3 Flash/Pro, Claude (via OpenRouter), GPT-4o, Qwen3-VL (8B/32B/235B), Qwen2.5-VL (7B/32B/72B), LLaMA-3.2 (11B/90B), LLaMA-4 Maverick, Grok-4.1-Fast, Pixtral-12B, Mistral Medium 3.1, Mistral Small 24B, Ministral (3B/8B), Nemotron-12B, Phi-4-Multimodal, Seed-1.6-Flash.

---

## Domain-Specific Metrics

Defined in paper Appendix A.1:

- **S_dis** — Disease Identification Score (strict entity match)
- **S_safe** — Safety Score (false-reassurance penalty)
- **S_clin** = 0.5·S_dis + 0.3·S_act − 0.2·(1 − S_safe) — composite clinical utility
- **S_vg** — Visual Grounding recall of expert-verified cues
- **E** — Explainability Efficiency (verified cues per 100 words)
- **B** — Prevalence Bias (Eq. 7)
- **F** — Cross-Class Fairness (Eq. 8)

---

## Supplementary Files

### `diseases_knowledge_base/`
Expert disease cards for each of the 116 disease categories across 34 crops. Each card contains:
- Disease description and causal agent
- Visual diagnostic criteria
- Severity progression markers
- Management recommendations

### `visual_cues/visual_cues.json`
24,950-entry lookup table mapping each `image_id` to expert-verified visual cues used for visual grounding evaluation.

---

## Reproducing Results

```bash
git clone https://github.com/SyedNazmusSakib/PlantInquiryVQA
cd PlantInquiryVQA
pip install -r requirements.txt
cp .env.example .env    # fill in API keys

# Download images from this HF repo
python scripts/download_images.py

# Run evaluation (Guided setting)
python eval/test_1_gemini3_flash.py

# Aggregate all results
python eval/compute_cascading_all_models.py
python eval/compute_fairness_all_models.py
```

---

## Licence

| Component | Licence |
|---|---|
| Code & eval scripts | [MIT](https://opensource.org/licenses/MIT) |
| Dataset annotations & QA pairs | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
| Source images | Retain upstream licences (see paper Appendix A.1 for all 39 component dataset licences — most CC BY 4.0; some CC0 / CC BY-NC 3.0) |

---

## Citation

If you use PlantInquiryVQA, please cite:

```bibtex
@article{sakib2026thinking,
  title={Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry},
  author={Sakib, Syed Nazmus and Haque, Nafiul and Amin, Shahrear Bin and Abdullah, Hasan Muhammad and Hasan, Md Mehedi and Hossain, Mohammad Zabed and Arman, Shifat E},
  journal={arXiv preprint arXiv:2604.20983},
  year={2026}
}
```

---

**Contact:** Open an issue on [GitHub](https://github.com/SyedNazmusSakib/PlantInquiryVQA) or reach out to the corresponding author listed in the paper.

*We thank Ali Akbar for large-scale data collection and Abdullah Shahriar for creating the figures and diagrams.*