Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

地球上的大多数动物都是昆虫，最近的报道表明它们的丰富度正在急剧下降。尽管这些报道来自各种各样的昆虫类群和地区，但是评估这种现象的程度的证据很少。昆虫种群的研究具有挑战性，大多数监测方法劳动强度大且效率低下。计算机视觉和深度学习的进步为应对这一全球挑战提供了潜在的新解决方案。相机和其他传感器可以在整个昼夜和季节周期内有效，连续且无创地进行昆虫学观察。标本的物理外观还可以通过实验室中的自动成像来捕获。在对这些数据进行训练后，深度学习模型可以提供昆虫丰度，生物量和多样性的估计。进一步，深度学习模型可以量化表型特征，行为和相互作用的变化。在这里，我们将深度学习和计算机视觉的最新发展与对昆虫和其他无脊椎动物的更具成本效益的监测的迫切需求联系在一起。我们介绍了基于传感器的昆虫监测实例。我们将展示如何将深度学习工具应用于超大型数据集以获取生态信息，并讨论在昆虫学中实施此类解决方案所面临的挑战。我们确定了四个重点领域，这将促进这一转变：1）验证基于图像的分类识别；2）生成足够的训练数据；3）开发公共的参考数据库；和4）整合深度学习和分子工具的解决方案。