LeafBench 🌿

LeafBench is a comprehensive visual question answering (VQA) benchmark derived from the LeafNet dataset, designed to systematically evaluate the capabilities of Vision-Language Models (VLMs) in understanding plant diseases. It supports six hierarchical diagnostic tasks — from binary health screening to expert-level taxonomic reasoning.

📄 Paper: LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases
🔗 Collection: LeafSight on Hugging Face
💻 Code: github.com/EnalisUs/LeafBench

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Dataset Summary

LeafBench is a curated subset of LeafNet built specifically for evaluating large vision-language models on plant pathology tasks. Rather than relying on open-ended generation (which is prone to hallucination), LeafBench employs a label-constrained prompting strategy where models must select from a closed-world candidate set. This ensures reliable output parsing and consistent, context-bounded inference.

The benchmark includes:

• Full (All): 2,910 images and 13,950 question-answer pairs
• Tiny subset: 890 samples — a cost-effective proxy for evaluating commercial APIs or establishing human expert baselines

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Supported Tasks

LeafBench evaluates models across six hierarchical diagnostic tasks:

Task	Abbreviation	Description
Healthy-Diseased Classification	HDC	Binary assessment of pathology presence
Disease Identification	DI	Identification of specific disease conditions
Pathogen Classification	PC	High-level causal agent (Fungal, Bacterial, Viral, Mite)
Crop Species Identification	CSI	Host plant species recognition across 22 species
Scientific Name Classification	SNC	Binomial-level scientific pathogen naming
Symptom Identification	SI	Fine-grained visual symptom recognition (lesion morphology, chlorosis, etc.)

All questions are visually dependent — correct answers cannot be derived from text alone and require analysis of the accompanying leaf image.

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Dataset Structure

Each sample in LeafBench contains:

{
  "image": "<PIL.Image>",          # RGB leaf image (JPG)
  "question": "<str>",             # Multiple-choice diagnostic question
  "choices": ["A. ...", "B. ...", "C. ...", "D. ..."],  # Candidate answer options
  "answer": "<str>",               # Ground-truth answer label (e.g., "A")
  "task": "<str>",                 # Task type: HDC | DI | PC | CSI | SNC | SI
  "species": "<str>",              # Crop species (e.g., "Apple", "Tomato")
  "disease": "<str>",              # Disease name (e.g., "Black Rot")
  "pathogenic_agent": "<str>",     # Pathogen category (e.g., "Fungal")
  "taxonomic_name": "<str>",       # Scientific name (e.g., "Botryosphaeria spp.")
  "symptom": "<str>",              # Symptom description (e.g., "Dark brown spots")
  "acquisition": "<str>"           # Image source: "Farm" or "Lab"
}

Data Splits

Split	Images	QA Pairs
All (Full Benchmark)	2,910	13,950
Tiny (Subset)	~190	890

QA Distribution by Task

Task	Sample Count
Healthy-Diseased Classification (HDC)	~2,325
Disease Identification (DI)	~2,325
Crop Species Identification (CSI)	~2,325
Scientific Name Classification (SNC)	~2,325
Pathogen Classification (PC)	~2,325
Symptom Identification (SI)	~2,325

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Dataset Creation

Source Data

LeafBench is derived from LeafNet, a large-scale dataset comprising:

• 186,000 expert-annotated leaf images
• 22 common crop species
• 62 disease categories (97 total fine-grained classes including healthy controls)
• Images collected from 7 countries across 3 continents (USA, India, Bangladesh, Kenya, Ghana, Tanzania, Vietnam)

Curation Process

1. Metadata synthesis: Biological taxonomies (species, disease, pathogenic agent, symptom descriptions) were sourced from authoritative repositories (NIH, USDA NIFA).
2. Expert verification: All image-metadata pairs underwent review by agricultural domain experts to filter mislabeled or noisy samples.
3. LeafBench extraction: A targeted subset was selected from LeafNet to represent the full range of diagnostic task difficulty.
4. Label-constrained QA construction: Questions were formulated as closed-form multiple-choice items requiring visual-dependent reasoning.

Annotation

• All annotations are expert-curated, not synthetically generated.
• Metadata fields include: species, disease name, pathogenic agent, taxonomic nomenclature, symptom description, image acquisition environment (Farm/Lab), and resolution.

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Evaluation

Metric

Accuracy (Acc) is the primary evaluation metric — the percentage of questions correctly answered by comparing model predictions to ground-truth labels.

Benchmark Results (Zero-Shot VQA)

Model	HDC	DI	CSI	SNC	PC	SI	Avg.
GPT-4o	92.48	85.27	85.58	65.27	56.47	51.64	72.78
Gemini 2.5 Pro	88.25	78.54	83.21	64.89	51.23	48.99	69.18
SCOLD (domain-specific)	96.28	95.85	84.73	41.64	37.83	77.92	72.38
Qwen 2.5 VL	81.05	52.60	63.06	41.50	60.92	43.14	57.04
LLaVA-NeXT	88.33	33.64	48.82	27.10	70.82	32.09	50.13
BLIP-2	62.36	48.49	64.15	28.02	54.88	31.59	48.25
CLIP	21.20	46.51	48.99	32.56	20.43	32.32	33.67
Random Baseline	50.47	24.07	26.20	25.51	26.14	26.18	~29.8

Results are reported on the full All benchmark under zero-shot evaluation.

Key Findings

• Binary HDC tasks are easiest, with top models exceeding 90% accuracy.
• Fine-grained tasks (SNC, SI, PC) are significantly harder — even frontier models struggle below 65% on SNC.
• Domain-specific models (SCOLD) outperform general VLMs on disease and symptom tasks but struggle with taxonomic reasoning.
• Generic open-source VLMs (CLIP, SigLIP2) frequently perform near random chance on fine-grained tasks.

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Limitations

• Geographic coverage, while spanning 7 countries, could be broader to ensure global transferability.
• Images are static RGB captures and do not capture temporal dynamics (disease progression over time).
• Text annotations are limited to disease names, pathogen taxonomy, and brief symptom descriptions — severity scales and growth stage metadata are not included.
• Models trained or evaluated on LeafBench may not generalize to novel field settings with phenotypic expressions outside the visible spectrum.

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Citation

If you use LeafBench in your research, please cite:

@article{leafnet2026,
  title     = {LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for 
               Foundational Vision-Language Understanding of Plant Diseases},
  author    = {Khang Nguyen Quoc and Phuong D. Dao and Luyl-Da Quach},
  journal   = {arXiv preprint arXiv:2602.13662},
  year      = {2026}
}

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License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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Contact

For questions or contributions, please open an issue on GitHub or contact the authors via the paper's corresponding email.