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

TL;DR

The paper addresses the challenge of noninvasively monitoring Goodman's mouse lemurs in expansive, semi-natural zoo habitats. It presents an IoT-enabled smart feeding station that integrates RFID-based identification, precise weight measurement, visit timing, and a selective trapping mechanism, all connected via LoRaWAN to a centralized web server. Field validation in Zurich Zoo's Masoala rainforest demonstrated high reliability: RFID detection at 98.68%, LoRa transmission at 97.99%, and weighing deviations below 0.41 g, across 60 days and 1995 data packets. The system supports automated population monitoring, reduced staff workload, and data-driven welfare research, representing a practical advance for noninvasive ecological monitoring in cryptic species.

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.

Figures (6)

• Figure 1: Final deployment of the smart feeding station at the zoo's Masoala rainforest.
• Figure 2: Overview of the final system with roof and side panel removed.
• Figure 3: Software overview illustrating the interconnection between the modules, differentiating between hardware (blue, red) and software (purple) connections.
• Figure 4: State machine of the weighing system, illustrated with an example measurement. The animal is detected when entering, causing the state machine to transfer from idle to entrance and finally to the weighing state. The weighing state searches for stability windows (green) until an exit state (weight difference larger than 20) is detected.
• Figure 5: Weight data graph over one night, illustrating how the weight increases at the beginning of the night and indicating instances of two lemurs being present simultaneously.