用窄谱光照结合图像识别，自动区分蜜蜂与寄生螨

Towards Varroa destructor mite detection using a narrow spectra illumination

• 用窄谱光增强蜂群成像对比度，提升螨虫可见性
• 基于U-net的语义分割模型实现蜜蜂与螨虫精准分离
• 为养蜂业提供非破坏性螨虫监测技术，适合科研与农业应用

本文聚焦于蜂箱监测设备的开发与改进，利用高光谱成像技术结合U-net语义分割架构及传统计算机视觉方法，实现对蜜蜂体内德国家庭螨（Varroa destructor）的检测。主要目标是收集蜜蜂与螨虫的数据集，并提出一种可区分蜜蜂与螨虫的计算机视觉模型。该方法通过窄谱光照增强图像对比度，提升微小螨虫的检测精度，为养蜂业提供一种非侵入式、自动化螨虫监测方案。

原文摘要 · Abstract (English)

This paper focuses on the development and modification of a beehive monitoring device and Varroa destructor detection on the bees with the help of hyperspectral imagery while utilizing a U-net, semantic segmentation architecture, and conventional computer vision methods. The main objectives were to collect a dataset of bees and mites, and propose the computer vision model which can achieve the detection between bees and mites.

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