BatEchoMon: India’s First Automated Bat Monitoring and Detection System, Pg18

Key Highlights

1. 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

2. 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 signatures

Mains Mock Question:

Q. Discuss how artificial intelligence and automation can aid biodiversity conservation in India. Illustrate with examples like BatEchoMon. (GS-3 | 250 words)