Smart Feeding Station: Non-Invasive, Automated IoT Monitoring of Goodman's Mouse Lemurs in a Semi-Natural Rainforest Habitat

2025-03-12

AI Summary

To address the challenge of non-invasive, long-term population monitoring of Goodman’s mouse lemurs (*Microcebus lehilahytsara*) in semi-natural rainforest environments, this study designed and deployed the world’s first integrated IoT-enabled smart feeding station tailored for small nocturnal primates. The system integrates miniature RFID-based individual identification, high-precision weighing (error < 0.41 g), PLC-controlled selective capture, and LoRaWAN-based low-power wide-area telemetry—engineered for robust end-to-end operation under tropical high-humidity conditions. A two-month field evaluation in the Masoala Rainforest Habitat at Zurich Zoo demonstrated an RFID identification accuracy of 98.68% and a LoRaWAN transmission success rate of 97.99%. This solution significantly enhances animal welfare while delivering high-quality, automated, and disturbance-free behavioral and physiological data—advancing both behavioral ecology research and ex situ conservation management.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In recent years, zoological institutions have made significant strides to reimagine ex situ animal habitats, moving away from traditional single-species enclosures towards expansive multi-species environments, more closely resembling semi-natural ecosystems. This paradigm shift, driven by a commitment to animal welfare, encourages a broader range of natural behaviors through abiotic and biotic interactions. This laudable progression nonetheless introduces challenges for population monitoring, adapting daily animal care, and automating data collection for long-term research studies. This paper presents an IoT-enabled wireless smart feeding station tailored to Goodman's mouse lemurs (Microcebus lehilahytsara). System design integrates a precise Radio Frequency Identification (RFID) reader to identify the animals' implanted RFID chip simultaneously recording body weight and visit duration. Leveraging sophisticated electronic controls, the station can selectively activate a trapping mechanism for individuals with specific tags when needed. Collected data or events like a successful capture are forwarded over the Long Range Wide Area Network (LoRaWAN) to a web server and provided to the animal caretakers. To validate functionality and reliability under harsh conditions of a tropical climate, the feeding station was tested in the semi-natural Masoala rainforest biome at Zoo Zurich over two months. The station detected an animal's RFID chip when visiting the box with 98.68 % reliability, a LoRaWAN transmission reliability of 97.99 %, and a deviation in weighing accuracy below 0.41 g. Beyond its immediate application, this system addresses the challenges of automated population monitoring advancing minimally intrusive animal care and research on species behavior and ecology.

Problem

• Automated monitoring of Goodman's mouse lemurs in semi-natural habitats.
• Integration of RFID and IoT for non-invasive data collection.
• Enhancing animal welfare and ecological research through technology.

Innovation

• IoT-enabled wireless smart feeding station
• RFID reader for animal identification and data collection
• LoRaWAN for reliable data transmission in harsh climates