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在蜂巢入口处视觉识别蜜蜂行为模式

Tomyslav Sledevič¹, Artūras Serackis¹, Dalius Matuzevičius¹

¹Department of Electronic Systems, Faculty of Electronics, Vilnius Gediminas Technical University - VILNIUS TECH, Vilnius, Lithuania.

|February 25, 2025

概括

这项研究引入了一种自动化系统,用于识别蜂巢入口的蜜蜂行为模式,使用YOLOv8. 该方法通过精确监测蜜蜂活动,减少手工检查和改进蜂巢管理来增强养蜂.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
动物行为动物行为

背景情况:

有效的养蜂和蜂巢管理依赖于了解蜜蜂的行为.
在蜂巢入口处手动监测蜜蜂活动是劳动密集型的,可能会扰乱蜂群.

研究的目的:

开发一种自动化系统,用于在蜂巢入口识别蜜蜂行为模式.
提高蜜蜂活动监测的准确性和效率,以改善养蜂.

主要方法:

利用YOLOv8模型进行对象检测和细分来分析蜜蜂行为.
开发了来自八个蜂巢的7200数据集,用于培训和验证.
在蜂巢入口区域内分析了蜜蜂的位置,方向,轨迹和速度.

主要成果:

在各种蜜蜂活动 (食,吹风,洗板,防御) 中,平均检测准确率为98%.
该系统以每秒高达36的速度运行,超越了现有的方法.
通过空间轨迹和密度图分析成功识别了重叠行为.

结论:

这种新的方法显著改善了对蜜蜂行为的自动监测.
减少手工检查的必要性,最大限度地减少干扰,提高蜂群健康.
为未来蜜蜂养殖中的自动化系统提供基础,以提高生产率.

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