Open-source tag-free monitoring of individual birds using automated weighing and deep-learning recognition

Effective animal monitoring is essential for assessing health, behavior, and environmental interactions, particularly in research and welfare contexts. This study presents a low-cost, open-source system designed for non-invasive monitoring of budgerigars ( Melopsittacus undulatus ), a small parrot species frequently used in animal behavior research. The system integrates a perch-based scale for voluntary weight measurement, a temperature sensor, and a camera for image capture, all controlled by a Raspberry Pi. By leveraging fine-tuned neural networks, the system achieves automated individual recognition with high accuracy, eliminating the need for invasive tagging methods. The modular design ensures accessibility, scalability, and minimal disturbance to the animals, while the accompanying software streamlines data collection, processing including labeling, and visualization. This approach provides a comprehensive solution for continuous monitoring, offering valuable insights for research and husbandry while prioritizing animal welfare.