Mankin Richard, Hagstrum David, Guo Min, Eliopoulos Panagiotis, Njoroge Anastasia
United States Department of Agriculture, Agricultural Research Service Center for Medical, Agricultural and Veterinary Entomology (CMAVE), Gainesville, FL 32608, USA.
Department of Entomology, Kansas State University, Manhattan, KS 66502, USA.
Insects. 2021 Mar 19;12(3):259. doi: 10.3390/insects12030259.
Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.
声学技术能提供有关储存昆虫行为、生理、数量和分布等方面难以获取的信息。例如，对隐藏在谷物中的未成熟昆虫进食进行声学检测有助于准确监测，因为它们可能比成虫数量更多，且在没有成虫的样本中也会存在。现代工程学和声学已被纳入储藏物昆虫管理的决策支持系统，但由于设备成本和解读所收集数据所需的技能，其应用受到一定限制。然而，廉价的现代工具可能有助于将声学技术进一步融入害虫管理和精准农业的主流。本文测试了这样一个系统，以描述谷象（鞘翅目：象甲科）成虫和幼虫在储存谷物中的活动及进食情况。开发改进方法以识别目标害虫昆虫的声音，将它们彼此区分并与背景噪声区分开来，是当前研究的一个活跃领域。最新方法中最强大的可能是机器学习。这些方法根据背景噪声类型和目标昆虫声音的信号特征具有不同的优缺点。它们很可能在未来促进检测自动化并降低储藏物昆虫管理成本。
