🔔 Alerts based on bee counter
🎯 Purpose
Generate intelligent alerts based on bee traffic patterns to notify beekeepers of potential issues requiring immediate attention or intervention.
🎭 User Story
- As a beekeeper managing multiple hives
- I want to receive timely alerts when unusual bee activity patterns are detected
- So that I can quickly respond to potential problems like robbing, disease outbreaks, or colony emergencies
🚀 Key Benefits
- Early intervention: Detect problems before they become critical
- Remote monitoring: Stay informed without constant physical presence at apiaries
- Pattern recognition: Identify subtle changes that human observation might miss
- Time efficiency: Focus attention on hives that actually need intervention
🔧 Technical Overview
Analyzes bee counting data and traffic patterns to identify anomalies indicating potential issues. Uses statistical analysis and machine learning to distinguish between normal variations and concerning patterns, triggering notifications through multiple channels.
📋 Acceptance Criteria
- Detects robbing behavior through abnormal incoming/outgoing ratios
- Identifies colony weakness through reduced activity patterns
- Recognizes disease indicators through traffic anomalies
- Sends alerts within 15 minutes of pattern detection
- Provides alert severity levels (info, warning, critical)
- Includes recommended actions in alert messages
🚫 Out of Scope
- Diagnostic certainty (alerts indicate probability, not definitive diagnosis)
- Automatic intervention (human judgment required)
- Long-term trend analysis (focuses on immediate alerts)
- Weather-related activity variations (basic filtering only)
🏗️ Implementation Approach
- Pattern analysis: Statistical deviation detection from baseline activity
- Alert triggers: Configurable thresholds for different alert types
- Notification system: Email, SMS, push notifications, dashboard alerts
- Context awareness: Consider time of day, season, and weather data
- Alert filtering: Prevent alert fatigue through intelligent grouping
📊 Success Metrics
- Alert accuracy >75% (confirmed issues vs false alarms)
- Response time <15 minutes from detection to notification
- User engagement rate >80% (alerts acted upon)
- False positive rate <25% to maintain user trust
- Critical issue detection rate >90%
🔗 Related Features
📚 Resources & References
- Beekeeping emergency response guidelines
- Statistical anomaly detection methods
- Alert system best practices for agricultural monitoring
💬 Notes
Key alert scenarios include robbing during dearth periods, wasp/hornet attacks on weak colonies, disease transmission indicators, and unusual activity suggesting colony distress. Sound analysis integration could enhance detection accuracy.