Publication
Airborne pollen cause seasonal allergies and the number of people affected increases yearly due to global warming and urbanization. Governmental pollen sensing stations are sampling traps which require manual pollen identification and counting by trained personnel in the lab. In the past years, a number of researchers and startups started working towards automated pollen measurements by exploring a wide range of techniques. Many solutions reported in the literature are expensive or work for a limited number of pollen species. In this paper, we present the design of a prototype of an automated and affordable pollen detection device built from off-the-shelf components.
The design consists of three subsystems operating in the field and communicating the data to the backend server: (1) a particle trap with automatic filtering, (2) a particle concentration subsystem, and (3) a digital transmitted light microscope with layer-wise focus. The prototype shows effective particle gathering, filtering and concentration in a tiny-sized area. As a result, we reduce particle loss and improve image quality taken by the optical system when searching and focusing on pollen grains. The test results show that our device achieves high efficiency with up to 150 l/min air flow rates, evaluates over 90 % of captured pollen grains, and achieves 1 h measurement delay on average (2 h at maximum).
The prototype collects raw time-stamped microscopic images of pollen with 5-60 depth layers per sample depending on the number of objects contained in one sample. All images are transmitted to the backend server where we run a pollen detection algorithm to extract individual pollen grains from every image. We achieve 0.90 average precision and F1-score of 0.88 when detecting pollen in the images of individual layers taken in the field. Our prototype successfully operated in the wild for 115 days between April and August 2019, and shows high stability under a wide range of varying weather conditions, little maintenance need and low device-to-device variation.
N. Cao, M. Meyer, L. Thiele, O. Saukh, Automated Pollen Detection with an Affordable Technology, In Proc. of the International Conference on Embedded Wireless Systems and Networks (EWSN), 2020
Related
Signup
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checkbox-analytics | 1 year | Set by the GDPR Cookie Consent plugin, this cookie records the user consent for the cookies in the "Analytics" category. |
cookielawinfo-checkbox-functional | 1 year | The GDPR Cookie Consent plugin sets the cookie to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 1 year | Set by the GDPR Cookie Consent plugin, this cookie records the user consent for the cookies in the "Necessary" category. |
CookieLawInfoConsent | 1 year | CookieYes sets this cookie to record the default button state of the corresponding category and the status of CCPA. It works only in coordination with the primary cookie. |
PHPSESSID | session | This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed. |
viewed_cookie_policy | 1 year | The GDPR Cookie Consent plugin sets the cookie to store whether or not the user has consented to use cookies. It does not store any personal data. |
Cookie | Duration | Description |
---|---|---|
mec_cart | 1 month | Provides functionality for our ticket shop |
VISITOR_INFO1_LIVE | 6 months | YouTube sets this cookie to measure bandwidth, determining whether the user gets the new or old player interface. |
VISITOR_PRIVACY_METADATA | 6 months | YouTube sets this cookie to store the user's cookie consent state for the current domain. |
YSC | session | Youtube sets this cookie to track the views of embedded videos on Youtube pages. |
yt-remote-connected-devices | never | YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. |
yt-remote-device-id | never | YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. |
yt.innertube::nextId | never | YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. |
yt.innertube::requests | never | YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. |
Cookie | Duration | Description |
---|---|---|
_ga | 1 year | Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors. |
_ga_* | 1 year | Google Analytics sets this cookie to store and count page views. |
_gat_gtag_UA_* | 1 min | Google Analytics sets this cookie to store a unique user ID. |
_gid | 1 day | Google Analytics sets this cookie to store information on how visitors use a website while also creating an analytics report of the website's performance. Some of the collected data includes the number of visitors, their source, and the pages they visit anonymously. |