STTR Phase I: Instrument Design for the Optical Detection of Insects in Agricultural Settings

这项小企业技术转让（STTR）第一阶段项目的广泛影响包括开发用于检测和管理农业部门昆虫种群的新工具和新方法。改进昆虫种群信息的好处是巨大的，包括通过避免虫害损害和改善传粉媒介活动来增加农业收入，以及通过提供可用于证明符合贸易伙伴农药残留限量和生物安全要求的信息来增加进入出口市场的机会。在成本方面，这种监测技术可以通过提高活动的针对性来提高农药施用的功效并减少对人类健康的影响。种植者可以进一步避免人工陷阱检查和现场调查的劳动力成本。最后，基于这种传感技术建立的决策支持系统可以提高用于指导农场投资决策的信息质量，作物保险公司和金融服务提供商可以使用该系统向种植者提供更具成本效益的服务。STTR第一阶段提出的项目将推进几个领域的科学和技术理解，包括光学工程模型的开发，以检测在复杂物理环境中移动的小生物的存在，先进的信号处理方法，从非目标物体中识别这些生物，以及通过与农业生产者和作物科学家合作，将用户驱动元素纳入设计和测试过程。昆虫监测是缓慢和劳动密集型的，使用的方法几十年来几乎没有改变。当观察到问题时，可能为时已晚，无法进行有效管理，或者选择可能代价高昂。检测害虫和有益昆虫（如传粉昆虫）的方法必须更新，以应对这些现实。该项目开发了一个系统来测量飞虫在果园和葡萄园等具有挑战性的农业环境中的分布。该仪器将使用获得专利的光探测和测距（LiDAR）方法来定位和分类三维空间中的昆虫。第一阶段项目的目标将是建立该系统准确表征生产农业环境中昆虫行为的能力，这将允许改进和发展新的昆虫时空分布生态模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Technology Transfer (STTR) Phase I project includes the development of new tools and methods for detecting and managing insect populations in the agricultural sector. The benefits of improved information about insect populations are substantial, including increased farm revenues through avoided pest damage and improved pollinator activity, and increased access to export markets by providing information that can be used to demonstrate compliance with the pesticide residue limits and biosecurity requirements of trading partners. On the cost side, this monitoring technology can improve the efficacy and reduce human health impacts of pesticide application through improved targeting of activities. Growers may further avoid labor costs of manual trap inspection and field surveys. Finally, decision support systems built upon this sensing technology can improve the quality of information used to guide farm investment decisions and can be used by crop insurers and financial service providers to deliver more cost-effective services to growers.The STTR Phase I proposed project will advance scientific and technological understanding in several areas, including development of optical engineering models to detect the presence of small organisms moving in complex physical environments, advanced signal processing approaches to discern these organisms from non-target objects, and the incorporation of user-driven elements into the design and testing process through collaborations with agricultural producers and crop scientists. Insect monitoring is slow and labor-intensive and uses methods that have changed little in decades. By the time problems are observed, it may be too late for effective management, or the options may be costly. Methods to detect pests and beneficial insects, such as pollinators, must be updated to deal with these realities. This project develops a system to measure distribution of flying insects in challenging agricultural settings such as orchards and vineyards. The instrument will use a patented Light Detection and Ranging (LiDAR) approach to locate and classify insects in three-dimensional space. The objective of the Phase I project will be to establish the ability of the system to accurately characterize insect behaviors in production agriculture settings, which will allow the improvement and development of new ecological models of insect distributions over space and time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.