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自动化蜜蜂亚种识别使用先进的翅膀分析和自适应层次集群

Shahram Dadgostar¹, Parsa Mobini Dehkordi², Saman Khodarahmi²

¹Khansar Honey Bee and Herbal Medicines Research Institute University of Isfahan Isfahan Iran.

Ecology and evolution

|September 5, 2025

概括

精确的蜜蜂亚种识别现在是可扩展和无标签的. 我们的新计算方法使用图像处理和聚类来可靠地区分亚种,

科学领域:

昆虫学与生物多样性科学
计算生物学和图像分析
保护遗传学

背景情况:

准确的蜜蜂亚种识别对于生物多样性和授粉至关重要.
像形态测量和深度学习这样的当前方法在可扩展性,主观性和可解释性方面都有局限性,特别是在混合群体中.
现有的技术难以与混合蜂群中的表型梯度分析平衡.

研究的目的:

开发一个可扩展的,无标签的蜜蜂亚种识别的新计算框架.
克服传统形态测量和监督深度学习方法的局限性.
为生态监测和蜜蜂种群保护提供实用工具.

主要方法:

整合自适应图像处理 (NLM无声化,CLAHE,自适应值,形态操作) 的管道,以隔离翅膀静脉.
张苏恩骨架化以提取基于图形的特征地图,捕捉静脉几何和拓.
适应性等级分类 (AHC) 用于无标签分类,包括杂交标本.

主要成果:

这个框架成功处理了26,481张机翼图像.
实现了0.72的轮得分, 这表明集群分离良好.
与混合数据集的传统形态测量技术相比,分类精度提高了26.1%.

结论:

拟议的方法为蜜蜂亚种的识别提供了可扩展和可解释的解决方案.
它有效地解决了区分杂交种群和表型中间体的挑战.
这种计算方法结合了手动和自动化方法的优势, 为环保主义者提供了宝贵的工具.

关键词:

自动分类生物多样性集群化蜜蜂图像处理翅膀的变化

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