Smart Beehive Project

AI Camera Detection
Multi-Sensor Data Collection
Remote Monitoring

Project Vision

The Smart Beehive combines AI camera detection, environmental sensors, and smartphone connectivity into one integrated monitoring system, all powered by a Raspberry Pi 5. It makes beekeeping accessible for beginners while providing powerful tools for large-scale operations.

Target Users

  • Beginner Beekeepers: Simplified hive management with guided suggestions and alerts
  • Commercial Operations: Scalable monitoring for managing dozens or hundreds of hives
  • Research Institutions: Detailed data collection for studying bee behavior and colony health

AI Detection

Camera system that identifies threats and analyzes bee behavior

Sensor Data

Temperature and humidity monitoring for hive health

Actionable Insights

Predictive suggestions help beekeepers make informed decisions

Hive Entrance — Camera Housing

I designed the entrance section of the beehive in Fusion 360. This is where the cameras will be mounted to monitor bee activity going in and out of the hive.

Initially I planned to use a single camera, but after researching cameras compatible with the Raspberry Pi 5 and looking at their focal lengths, I realized I'll need two cameras to get adequate coverage of the entrance. The plan is to run them off a Raspberry Pi 5 with the new Raspberry Pi AI Camera HAT.

V1 — First Design

The first version established the overall shape and dimensions of the entrance piece. I printed it in two sections so it would fit on the build plate.

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Smart Hive entrance V1 (half section) — designed in Fusion 360

📦 Download Entrance STL

V2 — Narrower Profile

After the first version, I decided to decrease the length of the part to make it narrower. This gives a tighter fit against the hive body and reduces wasted material.

Current Progress

I'm currently working on a version with an upper section to house the camera module. The goal is to have the camera securely mounted inside the entrance piece with a clear view of the landing board, all powered by a Raspberry Pi 5 with the new Raspberry Pi AI Camera HAT.

AI Detection Camera

A high-resolution entrance-mounted camera with onboard AI processing for real-time bee activity analysis. The weatherproof housing includes night vision capability.

Detection Capabilities

Entrance Monitoring

  • Bee activity pattern recognition
  • Pollen type identification and analysis
  • Unusual behavior detection (robbing, swarming)
  • Possible varroa mite detection and counting

AI Processing

  • Adaptive learning for individual hive behavior
  • Pattern recognition for health indicators
  • Predictive modeling for colony needs
  • Edge computing for real-time analysis

Environmental Sensors

A comprehensive sensor array for internal and external environmental monitoring, designed with bee-safe placement.

  • Temperature Monitoring: Internal and external sensors for climate control assessment
  • Humidity Sensors: Track moisture levels critical for bee health and honey curing
  • Weather Integration: Pairs with local forecasts to recommend optimal times for harvest or feeding

Real-Time Data Logging

All sensor data is tracked and logged in real time. Connect from your smartphone or other device to view live readings, historical trends, and receive alerts wherever you are.

Communication

WiFi and cellular connectivity for remote monitoring and data upload. The system is designed as a modular add-on compatible with standard Langstroth hive equipment.

Software

Remote Monitoring

Access hive data from any device with real-time alerts and notifications

Data Visualization

View historical trends and live sensor readings

Predictive Alerts

Notifications for feeding, harvesting, and health concerns

Multi-Hive Management

Scalable interface for managing multiple hives

Project Goals

For Beginners

  • Guided recommendations and early warnings
  • Educational insights about bee behavior
  • Confidence through data-driven decisions

For Commercial Operations

  • Scalable monitoring across many hives
  • Reduced inspection time and labor costs
  • Data-driven harvest timing

Future Enhancements

  • Integration with automated feeding systems
  • Swarm prediction and prevention alerts
  • Queen health monitoring through behavior analysis
  • Community data sharing for regional insights