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🌻 Detect bees with pollen for foraging statistics

🎯 Purpose​

Automatically detect and count bees carrying pollen loads at the hive entrance to track foraging activity and assess colony nutrition sources.

🎭 User Story​

  • As a beekeeper interested in colony foraging patterns
  • I want to automatically track how many bees return with pollen and when
  • So that I can understand nectar flows, evaluate forage availability, and monitor colony nutritional health

🚀 Key Benefits​

  • Foraging insights: Track seasonal foraging patterns and peak collection times
  • Nutrition monitoring: Assess colony access to diverse pollen sources
  • Early warning system: Sudden drops in pollen collection may indicate environmental issues
  • Research data: Valuable for understanding pollinator behavior and ecosystem health

🔧 Technical Overview​

Uses computer vision to identify distinctive pollen loads (corbiculae) on bee legs during entrance monitoring. Requires training dataset of bees with and without pollen loads, potentially using color analysis and morphological detection of leg-mounted pollen baskets.

📋 Acceptance Criteria​

  • Detects pollen loads with >80% accuracy under varying lighting conditions
  • Distinguishes between different pollen colors/sources (basic classification)
  • Counts pollen-carrying bees per hour/day
  • Integrates with existing bee counting pipeline
  • Provides pollen activity statistics over time
  • Functions reliably across different bee subspecies

🚫 Out of Scope​

  • Detailed pollen botanical identification (species-level classification)
  • Nectar load detection (internal, not visible)
  • Pollen quantity measurement (weight/volume)
  • Cross-referencing with local flora databases

🏗️ Implementation Approach​

  • Computer vision: Custom CNN model trained on pollen/no-pollen image dataset
  • Feature detection: Focus on bee leg area and corbiculae identification
  • Color analysis: HSV color space analysis for pollen identification
  • Integration: Extension of existing bee tracking algorithms
  • Training data: Leverage LabelBee platform examples and custom dataset creation

📊 Success Metrics​

  • Pollen detection accuracy >80% compared to manual annotation
  • Processing speed maintains real-time performance (30+ FPS)
  • False positive rate <15%
  • Seasonal pattern recognition capability
  • User adoption rate among beekeepers interested in foraging data

🔗 Related Features​

📚 Resources & References​

💬 Notes​

This feature builds on existing bee tracking infrastructure and adds significant value for understanding colony foraging behavior. Implementation complexity is moderate due to the need for specialized training data.