About
BeeAlarmed is an open-source project that brings AI-powered surveillance to beekeeping. Using a camera placed at the hive entrance, the system continuously captures and analyzes footage to detect, track, and classify individual bees using deep learning models built with TensorFlow and computer vision powered by OpenCV. The neural network can identify a range of critical bee characteristics and events — including bees carrying pollen packages, the presence of wasps, varroa mite infestations, and bees engaged in hive-cooling fanning behavior. This provides beekeepers and researchers with automated, real-time insights into colony health without manual inspection. The system is modular, featuring dedicated Python modules for detection (BeeDetection.py), classification (BeeClassification.py), tracking (BeeTracking.py), image handling, statistics, and visualization. It also integrates with LoRaWAN, enabling long-range wireless data transmission for IoT-connected hive deployments. Licensed under GPL-3.0, BeeAlarmed is entirely free and community-driven. It is best suited for technically inclined beekeepers, agricultural researchers, and developers interested in applying computer vision and machine learning to precision agriculture and biodiversity monitoring. Configuration is managed through a YAML file, making it relatively straightforward to adapt to different hardware setups.
Key Features
- Real-Time Bee Detection & Tracking: Uses OpenCV and filterpy to detect individual bees from camera footage and track their movement at the hive entrance.
- Neural Network Classification: A TensorFlow-based model classifies detected bees by characteristics such as pollen load, varroa mite presence, and species (bee vs. wasp).
- Hive Behavior Analysis: Identifies specific colony behaviors, including bees engaged in hive-cooling fanning, providing insight into hive temperature stress.
- LoRaWAN IoT Integration: Transmits hive monitoring data over long-range, low-power LoRaWAN networks, enabling remote deployment in fields and apiaries.
- Statistics & Visualization: Built-in statistics and visual output modules allow users to review detection results and trends over time.
Use Cases
- Automated early detection of varroa mite infestations to help beekeepers act before colony collapse.
- Monitoring hive entrances for wasp intrusions that threaten honey bee colonies.
- Tracking pollen foraging activity to assess the productivity and health of a colony.
- Remote apiary monitoring using LoRaWAN to send hive data from off-grid locations.
- Academic and agricultural research into bee behavior, colony dynamics, and AI-based biodiversity monitoring.
Pros
- Completely Free & Open Source: Licensed under GPL-3.0, BeeAlarmed is free to use, modify, and redistribute, with the full codebase available on GitHub.
- Multi-Threat Detection: Simultaneously monitors for multiple hive threats and behaviors — varroa mites, wasps, pollen collection, and cooling fans — in a single system.
- IoT-Ready with LoRaWAN: Supports wireless data transmission via LoRaWAN, making it suitable for off-grid or remote apiary deployments.
Cons
- Requires Hardware Setup: Users must supply and configure their own camera hardware and computing device (e.g., Raspberry Pi), which adds cost and complexity.
- Technical Expertise Needed: Installation and configuration require familiarity with Python, TensorFlow, OpenCV, and command-line environments.
- Limited Community & Support: As a small open-source project with 44 stars, community support and ongoing maintenance may be limited compared to commercial solutions.
Frequently Asked Questions
What hardware does BeeAlarmed require?
BeeAlarmed requires a camera (such as a Raspberry Pi camera module or USB webcam) placed at the hive entrance and a computing device capable of running Python and TensorFlow, such as a Raspberry Pi or a standard Linux computer.
What bee characteristics can BeeAlarmed identify?
The system can identify bees carrying pollen packages, the presence of wasps, varroa mite infestations on bees, and bees performing hive-cooling fanning behavior.
Is BeeAlarmed free to use?
Yes, BeeAlarmed is fully open source and licensed under the GPL-3.0 license, meaning it is free to use, study, modify, and distribute.
What technologies does BeeAlarmed use?
BeeAlarmed is built with Python and uses TensorFlow for neural network inference, OpenCV for image processing and computer vision, filterpy for tracking, and supports LoRaWAN for IoT data transmission.
Can BeeAlarmed operate in real time?
Yes, the system is designed for real-time monitoring. It captures frames from a live camera feed and processes them through detection, tracking, and classification pipelines continuously.
