About the System
Name: BatEchoMon (Bat Echolocation Monitoring)
Developers: Kadambari Deshpande and Vedant Barje
Institution: Long-Term Urban Ecological Observatory, Indian Institute for Human Settlements (IIHS), Bengaluru
Technology Used: Raspberry Pi microprocessor, ultrasonic detector, and neural network model
Core Functionality
Automatically activates at sunset
Detects and records bat calls in real time
Uses neural network to identify species
Isolates bat calls from other ambient sounds
Currently capable of identifying 6–7 common Indian bat species
Significance
A. Ecological Relevance
Fills a crucial gap in bat ecology and acoustic monitoring
Helps researchers shift from manual call analysis to automated classification
Aids conservation planning by improving understanding of bat distribution and behaviour
B. Technological Edge
Modular and portable design allows for customisation and scaling
Designed for long-term deployment in remote and urban habitats
Challenges and Future Scope
Limited current dataset – only 6–7 bat species
Need for more reference libraries and testing in diverse environments
Aim to expand species library and deploy BatEchoMon across multiple Indian ecosystems
Innovative Edge
Represents India’s move toward AI-led biodiversity tracking
Encourages data-driven wildlife policy
Can become a template for monitoring other species using acoustic signature
COMMENTS