Real-time Pollen Monitoring Systems Based On Enrichment And Microscopic Imaging

In recent years,with the increase of pollen allergy cases,the population has an increasing demand for monitoring the types and concentrations of pollen in the air.Traditional pollen particle monitoring relies on long-term sampling combined with manual visual inspection.The whole process is time-consuming and requires the participation of high professional personnel.As a result,it is difficult to establish the pollen monitoring sites in whole word.Optical automatic monitoring instruments can perform real-time analysis through single-particle optical signal detection.However,this type of analysis methods lacks the enriching samples process,which results in low detection accuracy for low-concentration pollen aerosols.Pollen sampling methods based on electrostatic and airflow drag forces combined with pollen microscopic imaging technology and artificial intelligence identification algorithm can realize real-time monitoring of low-concentration pollen with high capture efficiency,high accuracy and high automation.The whole system has the potential to be used for large-scale pollen monitoring around the world.The first chapter starts with pollen enrichment and analysis methods for the monitoring of pollen particles in the air,then summarizes the development and widely used commercial instruments in the field of pollen aerosol monitoring.For the pollen monitoring approaches under the enrichment-analysis mode,the passive and active pollen enrichment sampling methods,manual and automatic analysis techniques are introduced in detail,then the advantages and disadvantages of each technique are analyzed.For the flow-type pollen monitoring approach,the identification and counting of pollen particles are mainly realized by the scattering light and laser-induced fluorescence of single pollen particle,which can realize real-time detection and analysis.Finally,some automatic pollen identification systems based on optics and imaging principles are introduced.The second chapter describes a fully integrated real-time pollen monitor based on electrostatic capture and microscopic imaging.The monitor system integrates nondestructive sampling,electrostatic enrichment,automatic photo analysis,and selfcleaning modules.Through these modules,we have realized quantitative introduction of standard concentration of pollen aerosols,enrichment of pollen grains,real-time automatic detection based on machine vision,self-cleaning and recycling of detection area.In the pollen concentration range in three orders of magnitude,the system capture efficiency is stable at around 25%.The detection module that follows can continuously output microscopic images of captured pollen for subsequent automatic algorithm analysis.Furthermore,we present approaches for generating standard concentrations of particulate gases that can be transferred nondestructively into enrichment modules,which can be applied to quantitatively characterizing instrument performance.Compared with the traditional pollen analyzer,the pollen analyzer we developed realizes the automation of the whole process from collection to analysis.Moreover,it can obtain the pollen concentration in real time,which greatly saves labor costs and improves the analysis efficiency.In chapter three,an automatic pollen monitoring system based on electrostatic-drag force and multi-channel image identification is developed.Based on the principle of air amplification,we have designed and constructed an independent pollen transport module and realized the lossless transport of pollen particles.On the basis of electrostatic pollen capture,airflow drag force capture is added,which allows the pollen capture efficiency increasing to 63%,which realizes the highly efficient capture under high sampling flow.We have also developed a multi-channel microscopic imaging module to obtain the pollen images in bright and fluorescent field.For the pollen automatic identification,we applied the ratio of fluorescence intensity to preliminarily identify pollen species.At the same time,we developed the Alex Net algorithm based on deep learning to distinguish 7 species of pollen and the accuracy is over 97%.The newly developed pollen monitor realizes the whole process automatic of non-destructive transmission,efficient capture,fast multichannel microscopic imaging and artificial intelligence analysis of pollen in the ambient air,which can quickly obtain pollen information,providing technical support for building a large-scale pollen monitoring network.