| --- |
| 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**.* |
|
|
| [](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA) |
| [](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA) |
| [](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA) |
| [](https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA) |
| [](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.* |
|
|
