Deep learning and computer vision will transform entomology
Deep learning and computer vision will transform entomology
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DOI：
10.1073/pnas.2002545117
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发表时间：
2021-01-12
影响因子：
11.1
通讯作者：
Raitoharju, Jenni
中科院分区：
文献类型：
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作者：
Hoye, Toke T.;Arje, Johanna;Raitoharju, Jenni
地球上的大多数动物物种都是昆虫，最近的报告表明它们的数量正在急剧下降。虽然这些报告来自广泛的昆虫类群和地区，但评估这一现象程度的证据很少。昆虫种群的研究具有挑战性，大多数监测方法都是劳动密集型的，效率低下。计算机视觉和深度学习的进步为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昼夜和季节周期中有效，连续和非侵入性地进行昆虫学观察。标本的物理外观也可以通过实验室中的自动成像来捕获。当在这些数据上训练时，深度学习模型可以提供昆虫丰度、生物量和多样性的估计。此外，深度学习模型可以量化表型性状、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进行更具成本效益的监测的迫切需求联系起来。我们提出了基于传感器的昆虫监测的例子。我们展示了如何将深度学习工具应用于超大数据集以获取生态信息，并讨论了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）基于图像的分类识别的验证; 2）生成足够的训练数据; 3）开发公共的、精心策划的参考数据库;以及4）集成深度学习和分子工具的解决方案。
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.