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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.09238 |
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| _version_ | 1866915194503954432 |
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| author | Peter, Jonas Luder, Victor Davis, Leyla Rivero Schulthess, Lukas Magno, Michele |
| author_facet | Peter, Jonas Luder, Victor Davis, Leyla Rivero Schulthess, Lukas Magno, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Feeding Station: Non-Invasive, Automated IoT Monitoring of Goodman's Mouse Lemurs in a Semi-Natural Rainforest Habitat Peter, Jonas Luder, Victor Davis, Leyla Rivero Schulthess, Lukas Magno, Michele Software Engineering 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. |
| title | Smart Feeding Station: Non-Invasive, Automated IoT Monitoring of Goodman's Mouse Lemurs in a Semi-Natural Rainforest Habitat |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.09238 |