1. Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time‐consuming sorting and expert‐based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert‐based identification approach involving manual sorting and identification with an automatic image‐based technology.
2. We describe a robot‐enabled image‐based identification machine, which can automate the process of invertebrate sample sorting, specimen identification and biomass estimation. We use the imaging device to generate a comprehensive image database of terrestrial arthropod species which is then used to test classification accuracy, that is, how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) to move forward with the best possible image quality. We use state‐of‐the‐art Resnet‐50 and InceptionV3 convolutional neural networks for the classification task.
3. The results for the initial dataset are very promising as we achieved an average classification accuracy of 0.980. While classification accuracy is high for most species, it is lower for species represented by less than 50 specimens. We found significant positive relationships between mean area of specimens derived from images and their dry weight for three species of Diptera.
4. The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.

中文翻译：

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基于图像的自动识别和无脊椎动物生物量估计

1. 了解生物群落如何应对环境变化是生态和生态系统管理中的关键挑战。昆虫种群的明显减少需要更多的生物监测，但是费时的分类和基于专家的分类单元识别对可处理的昆虫样品数量构成了很大的限制。反过来，这完全影响了绘制和监测无脊椎动物多样性的工作规模。鉴于计算机视觉的最新进展，我们建议增强基于人类专家的标准识别方法，该方法包括使用基于图像的自动技术进行手动排序和识别。
2. 我们描述了一种基于机器人的基于图像的识别机，它可以使无脊椎动物样本分选，标本识别和生物量估计过程自动化。我们使用成像设备生成陆生节肢动物物种的综合图像数据库，然后将其用于测试分类准确性，即可以从机器拍摄的图像中预测标本的物种身份的程度。我们还测试了分类准确度对相机设置（光圈和曝光时间）的敏感性，以实现最佳图像质量。我们将最新的Resnet-50和InceptionV3卷积神经网络用于分类任务。
3. 初始数据集的结果非常有希望，因为我们实现了0.980的平均分类精度。尽管大多数物种的分类准确度很高，但少于50个标本代表的物种的分类准确度却较低。我们发现图像样本的平均面积与三种双翅目的干重之间存在显着的正相关关系。
4. 该系统是通用的，也可以轻松地用于其他类型的无脊椎动物。这样，我们的结果为在无脊椎动物的丰度，多样性和生物量方面产生更多关于时空变化的数据铺平了道路。