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，可引发多达 5 亿人的花粉热和哮喘。草的种类超过 10,000 种，但草的类型、位置和气候对空气中花粉的影响尚不清楚。这是一个关键问题，因为亚热带和温带草对环境因素的反应不同。该项目旨在在数码相机图像上使用人工智能来学习观察当地的草花，并将其与经过训练以检测草花粉类型的空气传感器相结合。预期成果是追踪空气中草花粉类型的新能力。这些结果可以改变花粉的监测方式，以减轻过敏负担，并提供空气花粉含量变化的证据。