Hyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The present paper introduces a method rooted in multivariate statistics designed to detect parasitic Varroa destructor mites on the body of western honey bee Apis mellifera, enabling easier and continuous monitoring of the bee hives. The methodology explores unsupervised (K-means++) and recently developed supervised (Kernel Flows - Partial Least-Squares, KF-PLS) methods for parasitic identification. Additionally, in light of the emergence of custom-band multispectral cameras, the present research outlines a strategy for identifying the specific wavelengths necessary for effective bee-mite separation, suitable for implementation in a custom-band camera. Illustrated with a real-case dataset, our findings demonstrate that as few as four spectral bands are sufficient for accurate parasite identification.

翻译：高光谱成像在农业中的应用日益普遍。这类图像具有更高的光谱分辨率优势，需要先进的光谱处理技术来挖掘其信息潜力。本文提出一种基于多元统计的方法，用于检测西方蜜蜂(Apis mellifera)体表的寄生性瓦螨(Varroa destructor)，从而实现对蜂巢更便捷、持续的监测。该方法探索了无监督(K-means++)和近期发展的有监督(核流-偏最小二乘法,KF-PLS)寄生识别技术。此外，鉴于定制波段多光谱相机的兴起，本研究还提出了确定有效分离蜜蜂与瓦螨所需特定波长的策略，适用于定制波段相机的实施。通过实际案例数据集的验证，我们的研究结果表明，仅需四个光谱波段即可实现准确的寄生识别。