The Vision: Automated Ethology for Citizen Science I am launching a project to monitor bee behavior and diversity in residential gardens using low-cost sensors and Edge AI. Our goal is to create an open-source, scalable "smart garden" kit that moves the needle from simple species identification to automated ethology—understanding not just who is in our gardens, but how they interact with their environment.

While projects like iNaturalist or PollinatorWatch exist, our focus is the temporal dimension of behavior, providing a standardized hardware/software stack for high-resolution ethological data at scale.

🚀 Why this project is a Game Changer

• From Identification to Behavior: We go beyond bounding boxes. Our models classify complex behaviors: foraging duration, flower constancy, and nesting patterns.
• Edge AI for the Masses: No $1000 setups. We optimize computer vision to run locally on low-power hardware.
  • Technical Debate: For the PoC, should we prioritize the Raspberry Pi Zero 2W for its Linux ecosystem and Python flexibility (enabling faster iteration), or go straight to the ESP32-S3 to leverage its native AI vector instructions and superior power management? If you've deployed vision-based sensors in the field, I’d love your take on the best balance between dev speed and autonomy.
• Passive "Set & Forget" Science: Autonomous, weatherproof stations enable continuous, passive data collection, capturing rare events that manual snapshots miss.
• Networked Urban Data: Synchronizing data across hundreds of gardens to track pollinator movement in response to climate variations or urban development.

🛠️ Proof of Concept (PoC) Roadmap The objective of this phase is to validate the end-to-end data pipeline—from raw optical capture to the extraction of actionable ethological insights.

Phase 1: Data Acquisition & Ground Truth Standardization

• Sampling Rate Optimization: We are investigating the "sweet spot" for frame rates. While 10-15 fps is ideal for low-power edge processing, would higher rates (30+ fps) be necessary to capture rapid ethological markers like wing-assisted landing or complex pollen handling?
• Ethological Labeling: Annotating sequences by behavioral states (Foraging, Hovering, Interaction, Nest Entry) rather than just static species tags.
• "Gold Standard" Curation: Building a benchmark dataset to serve as the baseline for all subsequent algorithmic iterations.

Phase 2: Edge Intelligence & Model Optimization

• Micro-Architecture Benchmarking: Comparative evaluation of compressed models (TinyYOLOv8, MobileNetV3-Small) and quantization (INT8/FP16) for real-time edge inference.
• Intelligent Trigger Pipeline: Developing motion-gated filtering to trigger deep analysis only during biological activity, drastically saving battery life.

Phase 3: Autonomous Device Engineering

• Industrial Design & Ruggedization: Designing an IP65-rated enclosure optimized for thermal dissipation with adjustable optical mounts for macro-focal lengths.
• Power Management: Integration of solar harvesting and "Deep Sleep" logic to ensure 100% field autonomy.

🤝 Looking for Core Team Collaborators:

• Machine Learning Engineers: Object detection and behavioral time-series analysis.
• Hardware Hackers: Low-power camera modules, battery management, and PCB design.
• Ecologists / Entomologists: To help validate behavioral metrics and scientific relevance.
• UI/UX Designers: To imagine a citizen-friendly interface that makes biodiversity data engaging.

If you’re passionate about bees and want to build something that moves from the lab to the backyard, let’s talk!