Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Center for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China.
Sensors (Basel). 2021 May 11;21(10):3329. doi: 10.3390/s21103329.
Insects constitute a very important part of the global ecosystem and include pollinators, disease vectors, and agricultural pests, all with pivotal influence on society. Monitoring and control of such insects has high priority, and automatic systems are highly desirable. While capture and analysis by biologists constitute the gold standard in insect identification, optical and laser techniques have the potential for high-speed detection and automatic identification based on shape, spectroscopic properties such as reflectance and fluorescence, as well as wing-beat frequency analysis. The present paper discusses these approaches, and in particular presents a novel method for automatic identification of mosquitos based on image analysis, as the insects enter a trap based on a combination of chemical and suction attraction. Details of the analysis procedure are presented, and selectivity is discussed. An accuracy of 93% is achieved by our proposed method from a data set containing 122 insect images (mosquitoes and bees). As a powerful and cost-effective method, we finally propose the combination of imaging and wing-beat frequency analysis in an integrated instrument.
昆虫在全球生态系统中占有非常重要的地位，包括传粉媒介、疾病载体和农业害虫，它们对社会都有重要影响。对这些昆虫的监测和控制至关重要，因此非常需要自动系统。虽然生物学家的捕获和分析是昆虫鉴定的金标准，但光学和激光技术具有基于形状、反射率和荧光等光谱特性以及翅膀振动频率分析进行高速检测和自动识别的潜力。本文讨论了这些方法，特别是提出了一种基于图像分析的新型自动识别蚊子的方法，因为昆虫会根据化学和吸力吸引的组合进入陷阱。介绍了分析过程的详细信息，并讨论了选择性。我们提出的方法从包含 122 个昆虫图像（蚊子和蜜蜂）的数据集实现了 93%的准确率。作为一种强大且具有成本效益的方法，我们最终提出了将成像和翅膀振动频率分析相结合的集成仪器。
