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AAFC Herbarium Darwin Core Extraction

Production-ready toolkit for extracting Darwin Core metadata from herbarium specimen images

Version License Python Documentation

📚 View Full Documentation - Complete guides, tutorials, and API reference

🎯 What This Does

Automatically extracts structured biodiversity data from herbarium specimen photographs using OCR and AI:

  • Reads labels (handwritten & printed) from specimen images
  • Extracts Darwin Core fields (scientific name, location, date, collector, etc.)
  • Outputs standardized data ready for GBIF publication
  • Provides review tools for quality validation

Example Workflow

📷 Herbarium Photo → 🤖 AI Extraction → 📊 Darwin Core CSV → 🌍 GBIF Publication

Input: Herbarium specimen image Output: Structured database record

catalogNumber,scientificName,eventDate,recordedBy,locality,stateProvince,country
"019121","Bouteloua gracilis (HBK.) Lag.","1969-08-14","J. Looman","Beaver River crossing","Saskatchewan","Canada"

🚀 Quick Start

# Install
git clone https://github.com/devvyn/aafc-herbarium-dwc-extraction-2025.git
cd aafc-herbarium-dwc-extraction-2025
./bootstrap.sh

# Process specimens
python cli.py process --input photos/ --output results/

# Review results (Quart web app)
python -m src.review.web_app --extraction-dir results/ --port 5002

📦 Current Release: v2.0.0

Specimen-Centric Provenance Architecture

What's New in v2.0.0

🔬 Specimen Provenance System - Complete lineage tracking from raw images through all transformations - Automatic deduplication at (image_sha256, extraction_params) level - Multi-extraction aggregation for improved field candidates - Content-addressed storage with S3 integration

📊 Production-Ready Infrastructure - Async web framework (Quart) for high-performance review - Docker containerization for reproducible deployments - Clean 8MB repository (97% size reduction from v1.x) - Migration tools with full rollback capability

🎯 Quality & Efficiency - Confidence-weighted field aggregation across extraction runs - Review workflow with specimen-level tracking - Progressive publication: draft → batches → final - Full backward compatibility with v1.x data

📚 Documentation & Migration - Complete release plan: docs/RELEASE_2_0_PLAN.md - Migration guide with safety guarantees - GBIF validation integration roadmap (v2.1.0) - Specimen provenance architecture doc

Why This Matters

Architectural shift: - From: Image-centric processing (lost specimen identity) - To: Specimen-centric provenance (complete lineage tracking)

Research impact: - Enables reproducible extraction pipelines - Supports iterative improvement with safety - Production-ready data quality management - Foundation for GBIF-validated publication (v2.1.0)

See CHANGELOG.md for complete release notes.

🔧 Installation

Requirements

  • Python 3.11+
  • macOS (Apple Vision OCR) or Linux/Windows (cloud APIs)

Setup

# Clone repository
git clone https://github.com/devvyn/aafc-herbarium-dwc-extraction-2025.git
cd aafc-herbarium-dwc-extraction-2025

# Install dependencies
./bootstrap.sh

# Check available OCR engines
python cli.py check-deps

✅ Apple Vision API works out-of-the-box (FREE, no API keys)

Windows/Linux

Requires cloud API keys. Copy .env.example to .env and configure:

# OpenAI (GPT-4o-mini for direct extraction)
OPENAI_API_KEY="your-key-here"

# Optional: Anthropic Claude, Google Gemini
# ANTHROPIC_API_KEY=""
# GOOGLE_API_KEY=""

See API_SETUP_QUICK.md for detailed setup.

💡 Core Features

Multi-Engine OCR Support

Engine Platform Cost/1000* Quality Notes
Apple Vision macOS FREE Medium Best for macOS users
GPT-4o-mini All ~$3.70 High Layout-aware, 16 fields
Tesseract All FREE Low Fallback option
Azure Vision All ~$2.00 Medium Cloud alternative

*Estimated from 500-specimen baseline ($1.85 actual = $3.70/1000)

Intelligent Pipeline Composition

Agent-managed optimization: - 🆓 Zero budget: Vision API → Rules engine (7 fields) - 💰 Small budget: GPT-4o-mini direct (16 fields, ~$3.70/1000 specimens) - 🔬 Research-grade: Multi-engine ensemble voting (cost varies by providers)

See agents/pipeline_composer.py for decision logic.

Darwin Core Output

v1.0 Fields (7): - catalogNumber, scientificName, eventDate, recordedBy - locality, stateProvince, country

v2.0 Fields (16): All v1.0 fields plus: - habitat, minimumElevationInMeters, recordNumber - identifiedBy, dateIdentified, verbatimLocality - verbatimEventDate, verbatimElevation, associatedTaxa

Review & Validation Tools

Web interface (recommended): 

python -m src.review.web_app --extraction-dir results/ --port 5002
# Access at http://127.0.0.1:5002

Terminal interface: 

python herbarium_ui.py --tui

📊 Data Publication

Ready to publish extracted data to GBIF via Canadensys:

  1. Export Darwin Core Archive: 

    python scripts/export_dwc_archive.py \
  --input deliverables/v1.0_vision_api_baseline.jsonl \
  --output dwc-archive/occurrence.txt

  2. Generate EML metadata: 

    python scripts/generate_eml.py \
  --title "AAFC Herbarium - Saskatchewan Flora" \
  --license CC0

  3. Upload to Canadensys IPT (browser-based, no installation)

  4. Automatic GBIF publication (24-48 hours)

See docs/DATA_PUBLICATION_GUIDE.md for complete workflow.

🧪 Quality & Accuracy

Phase 1 Baseline (500 Specimens)

OpenAI GPT-4o-mini: - scientificName coverage: 98.0% (490/500) - catalogNumber coverage: 95.4% (477/500) - Actual cost: $1.85 ($0.0037 per specimen) - Status: Production-quality baseline

OpenRouter FREE (20 Specimens): - scientificName coverage: 100% (20/20) - Cost: $0.00 - Status: Validates FREE models outperform paid baseline

v1.0 Apple Vision (2,885 Photos - DEPRECATED)

  • ScientificName coverage: 5.5% (159/2,885) - FAILED
  • Status: Replaced by GPT-4o-mini/OpenRouter approach

⚠️ All extracted data should be manually reviewed before publication

🎯 Use Cases

✅ When to Use This Tool

  • Digitizing physical herbarium collections
  • Creating GBIF-ready biodiversity datasets
  • Batch processing specimen photographs
  • Extracting structured data from label images

❌ Not Suitable For

  • Live plant identification (use iNaturalist)
  • Specimens without readable labels
  • Real-time field data collection

📚 Documentation

📖 View Full Documentation Site

Complete guides, tutorials, and reference: - 🚀 Getting Started - Installation and quick start - 📖 User Guide - Processing workflows and GBIF export - 🔬 Research - Methodology and quality analysis - 💻 Developer Guide - Architecture and API reference

Legacy documentation (being migrated to docs site): - Agent Orchestration Framework - Data Publication Strategy - Scientific Provenance Pattern ⭐ - API Setup Guide

🔄 Processing Workflow

graph LR
    A[Image] --> B[OCR Engine]
    B --> C[Text Extraction]
    C --> D[Rules Engine]
    D --> E[Darwin Core]
    E --> F[Review Interface]
    F --> G[GBIF Export]

Step-by-Step

  1. Prepare images in a directory
  2. Run extraction: python cli.py process --input photos/ --output results/
  3. Review results: Web or terminal interface
  4. Export data: Darwin Core CSV ready for GBIF

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Development Setup

# Install dev dependencies
uv sync --all-extras

# Run tests
pytest

# Lint code
ruff check . --fix

📋 System Requirements

  • Python: 3.11 or higher
  • Disk space: ~1GB for dependencies, ~5GB for image cache
  • Memory: 4GB minimum (8GB recommended for large batches)
  • OS: macOS (best), Linux, Windows

🔖 Version History

Current: v2.0.0 (October 2025) - Specimen-centric provenance architecture Previous: v1.1.1 (October 2025) - Accessibility improvements and Quart migration Earlier: v1.0.0 (October 2025) - Production baseline with Apple Vision API

See CHANGELOG.md for full version history.

📄 License

MIT License - see LICENSE file for details.

🙋 Support

🏆 Project Status

Production Ready ✅ - ✅ v2.0.0 specimen provenance architecture released - ✅ 500-specimen baseline @ 98% quality validated - ✅ 2,885 photos ready for full-scale processing - ✅ Repository optimized (8MB, 97% size reduction) - ✅ Docker containerization and async review interface - 📋 Next: v2.1.0 GBIF validation integration - 📋 Next: Full dataset processing with validated pipeline

Built for Agriculture and Agri-Food Canada (AAFC) Enabling biodiversity data digitization at scale

[AAFC]: Agriculture and Agri-Food Canada [GBIF]: Global Biodiversity Information Facility [DwC]: Darwin Core [OCR]: Optical Character Recognition [API]: Application Programming Interface [CSV]: Comma-Separated Values [IPT]: Integrated Publishing Toolkit [TDWG]: Taxonomic Databases Working Group