AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO

Ant detection is essential for ecological research, offering insights into biodiversity, habitat health, and environmental change. Traditional detection techniques rely on manual sampling methods, which are labor-intensive and time-consuming. Recent advances in autonomous, vision based systems show promise for insect monitoring, yet no dedicated, field ready solution exists for ant identification. In this work, we present AntPi , a deep learning based system for real-time detection and classification on a Linux development board. To the best of our knowledge, the system is trained on the first dedicated dataset for arboricolous ants, comprising five species and one morphotype, sourced from citizen science contributions and direct field captures. Our approach employs the “You Only Look Once” (YOLO) framework for efficient object detection, augmented with environmental sensors to enable correlation between climatic variables and ant activity. To evaluate performance and robustness, we compare AntPi with an alternative configuration, including controlled experiments using background-only images with artificial ant-like noise, and introduce a novel robustness indicator to assess reliability under realistic conditions. Experimental results demonstrate strong detection performance and confirm the feasibility of automated, in-field ant monitoring. • Developed ANTPI , a novel low-cost, edge-computing system for autonomous ant monitoring using DL. • Introduced a dataset for ant detection comprising five arboricolous species and one morphotype, sourced from field captures and citizen science contributions. • Benchmark of YOLO Detection Algorithms implementing the YOLO methodology for accurate efficient identification of ants in complex environments. • Successfully deployed the model on Raspberry Pi systems, evaluating inference speed, energy consumption, and perfor- mance, such as CPU load and RAM usage, across different configurations. • Integrated cloud-based remote monitoring with environmental sensor records, enabling scalable and automated ecological surveillance.