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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003929 |
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| author | Lorenzo Palazzetti Daniele Giannetti Antonio Verolino Donato A. Grasso Cristina M. Pinotti Francesco Betti Sorbelli |
| author_facet | Lorenzo Palazzetti Daniele Giannetti Antonio Verolino Donato A. Grasso Cristina M. Pinotti Francesco Betti Sorbelli |
| author_sort | Lorenzo Palazzetti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bd11ea4bf7eb4d6e90c78421e6a98d59 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-bd11ea4bf7eb4d6e90c78421e6a98d592025-08-23T04:47:48ZengElsevierEcological Informatics1574-95412025-11-019110338310.1016/j.ecoinf.2025.103383AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLOLorenzo Palazzetti0Daniele Giannetti1Antonio Verolino2Donato A. Grasso3Cristina M. Pinotti4Francesco Betti Sorbelli5Department of Computer Science and Mathematics, University of Perugia, Italy; Corresponding author.Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, ItalyDepartment of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, ItalyDepartment of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, ItalyDepartment of Computer Science and Mathematics, University of Perugia, ItalyDepartment of Computer Science and Mathematics, University of Perugia, ItalyAnt 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.http://www.sciencedirect.com/science/article/pii/S1574954125003929IoTAnt detectionComputer visionTechnological transferEdge computing |
| spellingShingle | Lorenzo Palazzetti Daniele Giannetti Antonio Verolino Donato A. Grasso Cristina M. Pinotti Francesco Betti Sorbelli AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO Ecological Informatics IoT Ant detection Computer vision Technological transfer Edge computing |
| title | AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO |
| title_full | AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO |
| title_fullStr | AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO |
| title_full_unstemmed | AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO |
| title_short | AntPi: A Raspberry Pi based edge–cloud system for real-time ant species detection using YOLO |
| title_sort | antpi a raspberry pi based edge cloud system for real time ant species detection using yolo |
| topic | IoT Ant detection Computer vision Technological transfer Edge computing |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003929 |
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