Preserving the number and diversity of insects is one of our society's most important goals in the area of environmental sustainability. A prerequisite for this is a systematic and up-scaled monitoring in order to detect correlations and identify countermeasures. Therefore, automatized monitoring using live traps is important, but so far there is no system that provides image data of sufficient detailed information for entomological classification. In this work, we present an imaging method as part of a multisensor system developed as a low-cost, scalable, open-source system that is adaptable to classical trap types. The image quality meets the requirements needed for classification in the taxonomic tree. Therefore, illumination and resolution have been optimized and motion artefacts have been suppressed. The system is evaluated exemplarily on a dataset consisting of 16 insect species of the same as well as different genus, family and order. We demonstrate that standard CNN-architectures like ResNet50 (pretrained on iNaturalist data) or MobileNet perform very well for the prediction task after re-training. Smaller custom made CNNs also lead to promising results. Classification accuracy of $>96\%$ has been achieved. Moreover, it was proved that image cropping of insects is necessary for classification of species with high inter-class similarity.

翻译：保护昆虫的数量和多样性是我们在环境可持续性领域最重要的社会目标之一。实现这一目标的前提是需要进行系统性且规模化的监测，以检测相关性并识别应对措施。因此，使用活体陷阱进行自动化监测至关重要，但迄今为止尚无能够提供足够详细图像数据以支持昆虫学分类的系统。本研究中，我们提出一种成像方法，作为多传感器系统的组成部分，该系统被开发为低成本、可扩展、开源且适用于传统陷阱类型的方案。其图像质量满足分类学树中分类所需的要求。为此，我们优化了光照和分辨率，并抑制了运动伪影。该系统以包含16种昆虫物种的数据集为例进行验证，这些物种涵盖同属、同科及同目不同层级。结果表明，标准CNN架构（如基于iNaturalist数据预训练的ResNet50或MobileNet）在重新训练后能够出色地完成预测任务，而更小型的定制CNN也取得了令人鼓舞的结果。分类准确率已达到>96%。此外，研究证明，对于类间相似度高的物种分类而言，对昆虫图像进行裁剪是必要的。