Resumo:
Climate change poses significant threats to bee populations, making continuous monitoring
of hive activity essential for both apiculture management and ecological research.
This work presents an innovative and non-invasive bee monitoring system that combines
machine learning and computer vision techniques for automated bee counting at hive entrances.
The system employs the YOLOv8n object detection model, implemented on an
embedded Raspberry Pi Zero 2W platform, enabling local processing without the need
for cloud transmission. The performance evaluation demonstrated a Mean Average Precision
(mAP50) of 0.987, with precision of 0.91, recall of 0.96, and an F1-score of 0.94
in bee detection tasks. Validation against the gold standard, through Pearson correlation
analysis, revealed a strong agreement (𝑟 = 0.93) between automated and manual counts.
The Bland-Altman analysis showed a small mean bias of 3.4 bees, with 95% limits of
agreement within ±1.96 SD. The automated approach eliminates the need for manual
observation, reduces labor costs and colony disturbance, while maintaining high measurement
accuracy, offering significant advantages for precision apiculture and ecological
monitoring initiatives.
