PDF(Pubmed)
Abstract：
Varroa mites, scientifically identified as Varroa destructor, pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune systems, reducing their lifespans, and even causing colony collapse. They also feed during the pre-imaginal stages of the honey bee in brood cells. Given the critical role of honey bees in pollination and the global food supply, controlling Varroa mites is imperative. One of the most common methods used to evaluate the level of Varroa mite infestation in a bee colony is to count all the mites that fall onto sticky boards placed at the bottom of a colony. However, this is usually a manual process that takes a considerable amount of time. This work proposes a deep learning approach for locating and counting Varroa mites using images of the sticky boards taken by smartphone cameras. To this end, a new realistic dataset has been built: it includes images containing numerous artifacts and blurred parts, which makes the task challenging. After testing various architectures (mainly based on two-stage detectors with feature pyramid networks), combination of hyperparameters and some image enhancement techniques, we have obtained a system that achieves a mean average precision (mAP) metric of 0.9073 on the validation set.
摘要：
瓦螨,科学上认定为Varroa析构函数,对养蜂构成重大威胁，并导致影响蜜蜂种群的最具破坏性的疾病之一。这些寄生虫附着在蜜蜂身上,以他们的脂肪组织为食，削弱他们的免疫系统,减少他们的寿命，甚至导致殖民地崩溃。它们还在蜜蜂的想象前阶段在育卵细胞中进食。鉴于蜜蜂在授粉和全球食物供应中的关键作用，控制瓦螨是当务之急。用于评估蜂群中瓦螨侵染水平的最常用方法之一是计数落在放置在菌落底部的粘性板上的所有螨。然而,这通常是一个手动过程，需要相当长的时间。这项工作提出了一种深度学习方法，用于使用智能手机相机拍摄的粘板图像来定位和计数瓦螨。为此,已经建立了一个新的现实数据集：它包括包含大量伪影和模糊部分的图像，这使得任务具有挑战性。在测试了各种架构（主要基于具有特征金字塔网络的两级检测器）之后，结合超参数和一些图像增强技术，我们已经获得了一个系统，该系统在验证集上实现了0.9073的平均精度（mAP）度量。
