IoT Bird Species Identification

Bird Species Identification and Motion Alert Using ESP32-CAM and CNN

IoT-ESP32-CAM Bird Species Identification

An IoT project using ESP32-CAM to identify bird species and provide motion alerts using CNN MobileNet V2 Architecture. This system allows for real-time bird watching and cataloging with minimal power consumption.

System Overview

The system captures images when motion is detected, processes them through a trained CNN model, and identifies bird species with high accuracy. It can also send alerts when specific species are detected.

Left: The ESP32-CAM hardware setup. Right: Example of bird species detection in action.

Technical Implementation

  • Hardware: ESP32-CAM for image capture and initial processing
  • Backend: Flask server for image processing and model inference
  • Model: MobileNet V2 architecture trained on bird species dataset
  • Interface: Simple PHP web interface for viewing detections and alerts
  • Power Management: Optimized for battery operation with solar charging capability

Key Features

  1. Motion-activated image capture to conserve power and storage
  2. Identification of over 200 bird species with >90% accuracy
  3. Alert system for rare or specified bird species
  4. Historical data logging with timestamps and locations
  5. Low-power operation suitable for remote deployment
Web dashboard showing recent bird identifications with confidence scores and timestamps.