Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources

多种空气传播草花粉源的数字集成智能传感

• 批准号: DP240103307
• 负责人: Prof Janet Davies
• 金额: $ 47.7万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240103307
• 关键词: Digitally; Integrated; Smart; Sensing; Diverse

Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to see local grass flowers and integrate this with air sensors trained to detect grass pollen types. The expected outcomes are new capacities to track airborne grass pollen types. These outcomes can transform how pollen can be monitored to reduce the burden of allergies, and provide evidence of changing airborne pollen loads.

草花粉是全球主要的室外过敏原来源，在多达5亿人中引发了干草和哮喘。尚不清楚草类型，位置和气候对花粉对花粉的影响。这是一个关键问题，因为亚热带和温带草对环境因素的响应有所不同。该项目旨在在数码相机图像上使用人工智能，以学会看到当地的草花，并将其与经过训练以检测草花粉类型的空气传感器整合在一起。预期的结果是追踪空气中的草花粉类型的新容量。这些结果可以改变如何监测花粉以减轻过敏的负担，并提供变化空气载花粉负荷的证据。