ACCENT: ACoustic Control of ENTomogical pests

该项目将开发基于信号处理和机器学习的方法，以检测飞行昆虫害虫物种及其捕食者，因为它们在进入陷阱时被记录下来。它将根据工业合作伙伴提供的记录信号在实验室中开发，然后可以用于后者正在开发的智能陷阱。这项工作对于帮助监测农业重要作物中的虫害袭击以及为缓解战略提供信息非常重要，这些战略可能涉及部署生物方法，例如捕食性昆虫，从而减少杀虫剂的使用，从而产生许多积极的环境和经济效益。研究小组将开发基于检测昆虫翅膀拍打声音中存在的主音和泛音的方法，这些声音具有高度的物种依赖性，因此可以作为识别目标物种的优秀标记。要克服的部分挑战是在其他环境噪声的背景下检测这些音调和泛音，这些噪声可以包括鸟叫，农业机械和车辆，雨水和其他天气引起的声音，甚至偶尔的谈话。这将通过将记录的声音分成大约1秒的短时间块（大约是昆虫飞入陷阱时听到声音的时间长度），然后采用双重方法来实现。其中一个链将使用经典的过滤方法来挑选出每个块中的音调和泛音，并根据这些成分对整体声级的相对贡献开发方法。另一种方法是将记录的信号呈现给人工智能（AI）系统，该系统利用机器学习来区分包含目标信号的样本。这里的部分挑战将是通过“训练”来“调整”控制人工智能系统学习的变量，以便实现最高的成功检测率，同时最大限度地减少错误检测。工业合作伙伴Agrisound Ltd将提供现场记录样本以及目标昆虫物种的实验室记录。他们将就目标物种的选择以及未来在他们目前正在开发的智能陷阱中采用和部署这些方法提供建议。伦敦金斯顿大学（KUL）的研究小组将开发信号分析和目标物种检测的方法，并将就此与Agrisound进行详细联系。虽然不是当前应用的一部分，但Agrisound与种植者和最终用户有着广泛的联系，可以实现正在开发的技术的实际部署和利用。

This project will develop signal processing and machine-learning based methods to detect flying insect pest species, as well as their predators, as they are recorded while entering traps. It will be developed in the laboratory based on recorded signals provided by the industrial partners and can then be employed in smart traps being developed by the latter. The work is important in helping to monitor insect pest attacks in crops of agricultural importance, as well as informing mitigation strategies that can involve the deployment of biological methods such as predatory insects and thereby reduce the use of insecticides leading to many positive environmental and economic benefits. The research team will develop methods based on detecting the main tones and overtones that are present in the sound of the beating insect wings, which are highly species-dependent and therefore can serve as an excellent marker for identifying target species. Part of the challenge to be overcome is to detect these tones and overtones against the background of other ambient noises, which can include bird calls, agricultural machinery and vehicles, rain and other sounds arising from the weather, and even occasionally conversations. This will be achieved by dividing the recorded sounds into short time chunks of approximately 1 second (around the length of time the sound of the insect is heard as it flies into the trap), and then adopting a two-fold approach. One strand will use classical filtering methods to pick out the tones and overtones in each chunk, and develop methods based on the relative contributions of these components to the overall sound level. The other will be based on presenting the recorded signals to Artificial Intelligence (AI) systems that utilise machine learning to distinguish samples that contain the target signals. Part of the challenge here will be to 'tune' the variables that control the learning of the the AI systems through 'training' so that the highest successful detection rates are achieved, while simultaneously minimizing false detections. The industrial partner Agrisound Ltd will help by providing samples of field recordings as well as laboratory recordings of target insect species. They will advise on the selection of target species, as well as regarding future incorporation and deployment of these methods in the smart traps that they are currently developing. The team at Kingston University London (KUL) will develop the methods for signal analysis and target species detection, and will liaise with Agrisound in detail regarding this. Although not forming part of the current application, Agrisound has a wide range of links with growers and end users, that can enable real-world deployment and utilisation of the technology being developed.