Abstract The monitoring of pests in the field or lab experiments allows to identify the variation of infection levels and to enhance the development of integrated pest management programs. The use of traps to capture insects is an alternative in different crops and regions. However, identification and manual counting of captured specimens is often time-consuming, requires taxonomic knowledge, and relies on the expertise of specialists. Therefore, the automation of this process could reduce cost, increase accuracy, and scalable the analysis. Current computer vision and artificial intelligence techniques can identify objects of interest in digital images in a timely and accurate manner. Hence, this paper presents a survey considering the following Computer Science digital research databases: ACM, IEEE, IET, DBLP, ScienceDirect, Scopus, SpringerLink, and Web of Science. We found three hundred studies, published between 2015 to 2019, of which thirty-three were selected based on the eligibility criteria. Results showed the use of convolutional neural network approaches, techniques to improve feature extraction, the lack of treatment to insect overlapping, and the non-use of instance segmentation via deep learning.

中文翻译：

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从数字图像中自动识别昆虫：一项调查

摘要 在田间或实验室实验中监测害虫可以识别感染水平的变化，并促进综合害虫管理计划的发展。使用陷阱捕捉昆虫是不同作物和地区的替代方法。然而，捕获标本的识别和手动计数通常很耗时，需要分类学知识，并依赖于专家的专业知识。因此，此过程的自动化可以降低成本、提高准确性并扩展分析。当前的计算机视觉和人工智能技术可以及时准确地识别数字图像中感兴趣的对象。因此，本文对以下计算机科学数字研究数据库进行了调查：ACM、IEEE、IET、DBLP、ScienceDirect、Scopus、SpringerLink、和科学网。我们发现了 2015 年至 2019 年间发表的 300 项研究，其中 33 项是根据资格标准选择的。结果表明使用了卷积神经网络方法、改进特征提取的技术、缺乏对昆虫重叠的处理以及未使用通过深度学习进行的实例分割。