Smart IoT-based snake trapping device for automated snake capture and identification.

Area of Science:

• Engineering
• Computer Science
• Public Health

Background:

• Snakebites pose a significant public health threat, especially in tropical and subtropical areas, necessitating innovative mitigation strategies.
• Current methods for managing human-snake interactions are often reactive and may not prevent bites effectively.

Purpose of the Study:

• To develop and evaluate an innovative, non-invasive, IoT-based smart snake-trapping device for autonomous snake capture and identification.
• To enhance public safety in snakebite-prone regions by reducing human-snake encounters and enabling timely intervention.

Main Methods:

• The device integrates Internet of Things (IoT) and artificial intelligence (AI) with sensors (heat, vibration) and a camera to attract and detect snakes.
• A deep learning-based real-time classification algorithm, utilizing transfer learning with a convolutional neural network (CNN), identifies snakes with 91.3% accuracy.
• The system triggers appropriate actions, such as alerting authorities or safely releasing non-venomous snakes, and provides real-time user notifications via a smartphone application.

Main Results:

• The smart snake-trapping device successfully demonstrated autonomous capture and identification of snakes.
• The AI classification algorithm achieved a high accuracy of 91.3% in distinguishing between venomous and non-venomous species.
• The IoT integration enabled real-time data transmission and alerts, facilitating prompt responses to potential threats.

Conclusions:

• The developed IoT-based smart snake-trapping device offers an automated, efficient, and innovative solution for snakebite prevention.
• The system has the potential to significantly reduce snakebite incidents and contribute to ecological studies by monitoring snake populations.
• This technology can be effectively implemented in snakebite-prone areas, improving both public safety and wildlife management.