Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources

草花粉是全球室外过敏原的主要来源，可引发多达5亿人的枯草热和哮喘。有超过10,000种，草的类型，位置和气候对空气中的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。该项目旨在利用数码相机图像上的人工智能来学习看到当地的草花，并将其与训练有素的空气传感器相结合，以检测草花粉类型。预期的结果是追踪空气传播的草花粉类型的新能力。这些结果可以改变花粉的监测方式，以减轻过敏的负担，并提供空气中花粉负荷变化的证据。

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.