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Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance

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  • Tomyslav Sledevič

    (Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania)

Abstract

Thermoregulatory fanning behavior in honeybees is a vital indicator of colony health and environmental response. This study presents a novel dataset of 18,000 annotated video frames containing 57,597 instances capturing fanning behavior at the hive entrance across diverse conditions. Three state-of-the-art single-shot object detection models (YOLOv8, YOLO11, YOLO12) are evaluated using standard RGB input and two motion-enhanced encodings: Temporally Stacked Grayscale (TSG) and Temporally Encoded Motion (TEM). Results show that models incorporating temporal information via TSG and TEM significantly outperform RGB-only input, achieving up to 85% mAP@50 with real-time inference capability on high-performance GPUs. Deployment tests on the Jetson AGX Orin platform demonstrate feasibility for edge computing, though with accuracy–speed trade-offs in smaller models. This work advances real-time, non-invasive monitoring of hive health, with implications for precision apiculture and automated behavioral analysis.

Suggested Citation

  • Tomyslav Sledevič, 2025. "Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance," Agriculture, MDPI, vol. 15(15), pages 1-18, July.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1609-:d:1709970
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