Visual Machine Learning and Artificial Intelligence Application in Aeroallergen Identification During Spring, Summer, and Fall Pollen Season

Abstract

Continued validation of automated pollen identification (API) is needed. We performed a side-by-side device comparison of pollen counts identified using a Burkard sampler (BS) and an Automate Pollen Sampler (APS) by Pollen SenseTM. The designated gold standard device for comparison, the BS, was co-located with the APS in Waterloo, Iowa. Pollen collection, processing, and identification were performed following NAB requirements. The APS was provided by Pollen SenseTM. The APS collects particulate matter volumetrically from ambient air, automatically images the particulates, and uses a convolutional neural network to identify the individual pollen species. For this first iteration, 57 days were compared in the fall 2019 and spring-summer 2020. The top 10 pollen reported to the NAB in the upper Midwest were chosen for reporting. Accuracy is determined as the number of days a species was identified as present or absent in the APS that matches the Burkard, divided by the number of counting days x 100. Accuracy: ragweed 96, mulberry 50, juniper 8, oak 46, nettle 0, grass 65, ash 58, elm 8, pine 65, birch 23. Current accuracy of APS needs continued improvement. Varying correctness in pollen species identification rely on several factors including whether the APS was trained for that pollen species, morphology complexity, and amount of debris in the background. Identification and enumeration using visual machine learning methods in artificial intelligence is a process that improves automatically through experience. It requires multiple iterations of repetitive learning to achieve desired accuracy.