How an unexpected visitor in our backyard owl box led to years of photos, a lot of honey, and eventually a machine learning pipeline that can tell the difference between brood and breakfast.
It started with a simple discovery: bees had moved into our backyard owl box without permission. Four years later, I had transformed from accidental beekeeper to honey harvester—and accumulated a digital disaster that would make any data scientist cringe.
The Reality Check:
When you’re knee-deep in managing actual bees, photo organization feels like a luxury. But as someone who professionally untangles messy datasets, I knew this chaos was hiding valuable insights:
The irony wasn’t lost on me—I help organizations make sense of their data for a living, yet my own beekeeping records were a disaster.
Every digital photo contains metadata—timestamps, location data, camera settings. What if I could use this hidden information to reconstruct our beekeeping history without relying on my clearly unreliable memory?
The Hypothesis: Photo timestamps + clustering algorithms = automatic inspection timeline
# Extract EXIF metadata from all photos
photo_metadata = extract_exif_data(photo_directory)
# Cluster photos taken within 4 hours as same inspection
inspection_groups = cluster_by_time(photo_metadata, threshold_hours=4)
# Result: Automatic reconstruction of inspection history
timeline = create_inspection_timeline(inspection_groups)
The beauty of this approach: it works retroactively on years of unorganized photos.
Interactive timeline: Hover over points to see inspection details, photo counts, and notes
What the Data Revealed:
With our timeline established, the next question emerged: What can a computer actually see in a beehive photo?
I decided to run Google Cloud Vision API on our entire photo collection to test its limits. Could it distinguish honey from brood? Recognize individual bees? Detect the geometric patterns of healthy comb?
Each photo gets analyzed through six different Vision API endpoints:
{
"labels": [
{"description": "Honeybee", "confidence": 0.94},
{"description": "Insect", "confidence": 0.87},
{"description": "Food", "confidence": 0.73}
],
"dominant_colors": [
{"color": {"red": 240, "green": 200, "blue": 100}, "pixel_fraction": 0.35},
{"color": {"red": 220, "green": 220, "blue": 220}, "pixel_fraction": 0.25}
],
"objects": [
{"name": "Insect", "confidence": 0.82, "bounding_box": [...]}
]
}
The raw API responses contain gold mines of structured data about our hives.
Raw computer vision results need translation into meaningful beekeeping insights. I developed heuristics to convert colors and patterns into hive component estimates:
The Translation Layer:
Building the analysis pipeline revealed that I was asking the wrong questions. Instead of “How do I organize photos?”, the data led me toward much more interesting territory.
Despite feeling like our inspection schedule was chaotic, the timeline revealed hidden patterns:
Emerging Patterns:
The number of photos per inspection varies dramatically—from quick 3-shot checks to extensive 28-photo documentation sessions. What drives this behavior?
Initial Observations:
The computer vision results challenged my assumptions about what makes a “good” bee photo: Image being updated
Surprising Discoveries:
High Confidence Detection (Score: 0.94)
Surprising High Confidence (Score: 0.87)
The AI was detecting structural patterns I wasn’t consciously noticing.
For fellow data scientists curious about implementation:
# 1. Photo Discovery & Metadata Extraction
photos = discover_photos(directories)
metadata = extract_exif_parallel(photos)
# 2. Temporal Clustering
inspections = cluster_by_timestamp(metadata, threshold_hours=4)
# 3. Vision API Analysis
for inspection in inspections:
for photo in inspection.photos:
api_results = analyze_with_vision_api(photo)
beekeeping_insights = translate_to_hive_metrics(api_results)
# 4. Aggregation & Analysis
timeline_data = aggregate_inspection_metrics(inspections)
patterns = detect_seasonal_trends(timeline_data)
# 5. Interactive Visualization
charts = generate_plotly_visualizations(timeline_data, patterns)
Key Technical Decisions:
Converting raw API responses into domain-specific insights is requiring combining computer vision with beekeeping knowledge, for now this is a skeleton of my approach and experiments will be updated here soon. For now…
def analyze_hive_health(vision_results):
"""Convert Vision API results to beekeeping insights"""
# Color-based component detection
honey_pixels = count_pixels_in_range(vision_results.colors, HONEY_RGB_RANGE)
brood_pixels = count_pixels_in_range(vision_results.colors, BROOD_RGB_RANGE)
# Confidence aggregation
bee_confidence = aggregate_bee_labels(vision_results.labels)
# Pattern recognition
comb_quality = detect_hexagonal_patterns(vision_results.shapes)
return HiveHealthMetrics(
honey_ratio=honey_pixels/total_pixels,
brood_activity=brood_pixels/total_pixels,
bee_presence_confidence=bee_confidence,
comb_structure_quality=comb_quality
)
This translation layer transforms generic computer vision into actionable beekeeping intelligence.
Part 1 established the foundation—we can extract structure from chaos and teach machines to see hives. But the real magic happens when we can navigate through time and spot patterns across seasons and years.
Coming in Part 2: Building the Time Machine
Try It Yourself Preview
Want to test computer vision on your own photos? I’ve built a streamlined demo app:
🔗 Beehive Photo Analyzer - Upload any photo and see what the AI detects
This project demonstrates core principles that apply far beyond beekeeping:
🔄 Retroactive Structure Discovery: Sometimes the best datasets already exist—they just need the right tools to reveal their structure.
🤖 API-Powered Analysis: Modern computer vision APIs can provide sophisticated analysis without building models from scratch.
📊 Domain Translation: Raw AI results become valuable when combined with subject matter expertise.
📈 Progressive Enhancement: Start with basic organization, then layer on advanced analysis as patterns emerge.
Whether you’re drowning in family photos, business documents, or research images, the same principles apply: metadata contains stories, clustering reveals patterns, and modern AI can see things humans miss.
🐙 GitHub Repository: Complete analysis pipeline and visualization code is being re-written, previous version here
📊 Interactive Demo: Try the photo analyzer yourself
📝 Technical Deep-Dive: Jupyter notebook with full reproducible analysis - Coming soon here
# Clone the analysis pipeline
git clone https://github.com/dagny099/beehive-tracker
cd beehive-tracker
# Install dependencies
pip install -r requirements.txt
# Set up Google Cloud Vision API credentials
export GOOGLE_APPLICATION_CREDENTIALS="path/to/your-key.json"
# Run analysis on your photos
python analyze_photos.py --input-dir /path/to/photos --output timeline.html
# Open timeline.html in browser to explore results
What You’ll Need:
Next time: Building an interactive timeline that transforms four years of beekeeping chaos into explorable, clickable insights.
What stories are hiding in your photo collections? Share your ideas in the comments—I’d love to help you uncover the patterns in your visual data.
Barbara is a Certified Data Management Professional (CDMP) who discovered that the intersection of data science and beekeeping produces both honey and insights. Follow her journey at [barbhs.com] and try the photo analyzer at [hivetracker.barbhs.com].