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🐝 Detect swarming

🎯 Purpose​

Automatically detect swarm preparation and swarming events at the hive entrance to provide early warning for beekeepers to take preventive action.

🎭 User Story​

  • As a beekeeper concerned about losing valuable bee colonies
  • I want to receive early alerts when my hives show signs of swarming preparation
  • So that I can intervene with management techniques to prevent swarm departure and colony loss

🚀 Key Benefits​

  • Colony preservation: Prevent valuable bee colony losses through early detection
  • Proactive management: Enable timely intervention with swarm prevention techniques
  • Seasonal insights: Track swarming patterns and seasonal triggers
  • Economic protection: Avoid production losses from reduced colony strength

🔧 Technical Overview​

Analyzes unusual activity patterns, increased traffic volumes, and behavioral changes at the entrance that precede swarming events. Combines bee counting data with movement pattern analysis to detect characteristic pre-swarm and swarm behaviors including scout bee activity and mass exodus patterns.

📋 Acceptance Criteria​

  • Detects pre-swarm behavior indicators 24-48 hours before swarm departure
  • Identifies actual swarming events with >85% accuracy
  • Distinguishes between swarming and other high-activity events (robbing, orientation flights)
  • Provides real-time alerts through notification system
  • Tracks swarm probability scoring over time
  • Functions across different bee subspecies and seasonal conditions

🚫 Out of Scope​

  • Inside-hive queen cell detection (requires robotic beehive integration)
  • Weather correlation analysis (basic environmental data only)
  • Automatic swarm prevention actions (alert system only)
  • Post-swarm colony assessment and recovery recommendations

🏗️ Implementation Approach​

  • Pattern analysis: Statistical analysis of traffic volume anomalies and timing patterns
  • Behavior detection: Machine learning model trained on known swarm events
  • Alert system: Integration with existing notification infrastructure
  • Data sources: Bee counting, movement patterns, interaction frequencies
  • Validation: Historical data analysis and field testing during swarm season

📊 Success Metrics​

  • Early warning accuracy >80% for pre-swarm detection
  • Swarm event detection accuracy >85%
  • False positive rate <20% to maintain user trust
  • Alert delivery time <5 minutes from detection
  • Seasonal pattern recognition across multiple apiaries

🔗 Related Features​

📚 Resources & References​

  • Research on pre-swarm behavioral indicators
  • Seasonal swarming pattern studies
  • Beekeeping swarm prevention techniques

💬 Notes​

Critical feature for commercial beekeepers where swarm losses represent significant economic impact. Requires extensive training data from multiple seasonal swarm events.