1. Bioacoustic methods play an increasingly important role for the detection of insects in a range of surveillance and monitoring programmes.
2. Weak-flying insects evade detection because they do not yield sufficient audio information to capture wingbeat and harmonic frequencies. These inaudible insects often pose a significant threat to food security as pests of key agricultural crops worldwide.
3. Automatic detection of such insects is crucial to the future of crop protection by providing critical information to assess the risk to a crop and the need for preventative measures.
4. We describe an experimental set-up designed to derive audio recordings from a range of weak-flying aphids and beetles using an LED array.
5. A rigorous data processing pipeline was developed to extract meaningful features, linked to morphological characteristics, from the audio and harmonic series for six aphid and two beetle species.
6. An ensemble of over 50 bioacoustic parameters was used to achieve species discrimination with a success rate of 80%. The inclusion of the dominant and fundamental frequencies improved prediction between beetles and aphids because of large differences in wingbeat frequencies.
7. At the species level, error rates were minimized when harmonic features were supplemented by features indicative of differences in species' flight energies.

中文翻译：

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解决飞行中弱飞昆虫的识别：严谨的数据处理与生物学之间的耦合

1. 生物声学方法在一系列监测和监测计划中对昆虫检测发挥着越来越重要的作用。
2. 弱飞昆虫逃避检测，因为它们不能产生足够的音频信息来捕捉翼拍和谐波频率。作为世界范围内主要农作物的害虫，这些听不见的昆虫通常对粮食安全构成重大威胁。
3. 通过提供评估作物风险和预防措施需求的关键信息，自动检测此类昆虫对作物保护的未来至关重要。
4. 我们描述了一种实验装置，旨在使用 LED 阵列从一系列飞行能力较弱的蚜虫和甲虫中获取录音。
5. 开发了一个严格的数据处理管道，以从六种蚜虫和两种甲虫物种的音频和谐波系列中提取与形态特征相关的有意义的特征。
6. 使用超过 50 个生物声学参数的集合来实现物种区分，成功率为 80%。由于翼拍频率的巨大差异，包含主要频率和基本频率改进了甲虫和蚜虫之间的预测。
7. 在物种层面，当谐波特征由指示物种飞行能量差异的特征补充时，错误率被最小化。