# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 1 Introduction  
Pollen is a powdery substance produced by plants during their reproductive season. While pollen is essential for plant survival and reproduction, certain species, especially those that use wind for pollen dispersal, are the cause of most seasonal allergies in humans [1]. The first reports of pollen as a possible cause for several illnesses date back to the beginning of the XIX century with researchers such as [PERSON] and [PERSON] [2]. [PERSON] experimented with several instruments to sample the air for pollen. He exposed glass slides coated in a glycerin mixture for a period of 24 h after which he analyzed them using a microscope. This was performed daily from April to August to record the number of pollen grains captured. These first experiments are the basis for what the field of palynology has researched for the following 150 years. A major improvement to the study of air-borne pollen was the development of the [PERSON] volumetric trap [3]. His new system involved an air pump and a roll of tape coated in Vaseline which moved at a constant speed. This small change to the sampling method increased the amount of information that could be extracted by increasing the temporal resolution from daily to hourly concentration values that could be correlated with variations in weather. Although the instrument designed by [PERSON] is over 70 years old, it is still the standard when it comes to pollen monitoring. The problem with the classical way of pollen monitoring has become apparent in recent years with the introduction of pollen monitoring networks. The increase in the number of pollen traps at a national or international level has forced aerobiologists to spend more and more of their time on pollen counting. Other important aspects of pollen monitoring are the lack of reliability of humans when it comes to pollen counting and classification [4] and the lag introduced by the need to have humans in the loop. The lack of scalability of the Hirst-type systems is the reason why in recent years a move towards automated systems has started. The Rapid-E, an instrument developed by Plair ([[http://www.plair.ch/Rapid-E.html](http://www.plair.ch/Rapid-E.html)]([http://www.plair.ch/Rapid-E.html](http://www.plair.ch/Rapid-E.html)), accessed on 1 November 2021), is one such automated system that is capable of counting and analyzing particles autonomously. This device uses multiple lasers to measure the size, morphology and fluorescence spectrum of the aerosol particles. Automatic pollen classification using machine learning techniques has been proposed as far back as 1968 [5]. The first and the most numerous attempts at pollen classification have been using microscope photography as the input for machine learning models. While, so far, most of the work has been focused on microscope photography, many approaches have been explored throughout the years, such as fluorescence microscopy, flow cytometry, photoacoustic microscopy, fluorescence spectroscopy, Raman microscopy, Fourier-transform infra-red (FTIR), laser ablation mass spectrometry (LAMS) and elastically scattered light [6]. This work focuses on pollen classification using data from an automatic particle analyzer that uses air flow cytometry and fluorescence spectrum to characterize particles. The first use of deep learning techniques on such datasets was presented by [7], but were performed on a prototype device that was not widely available to use as a data source for automatic pollen monitoring. The first paper to introduce datasets from a commercially available device, Rapid-E, is [8], published in 2019. This area of research was expanded in [9] with better architecture selection and in [10] with the introduction of a new pollen dataset. This area of research is still in its early stages with many unknowns regarding the best use of the features provided by instruments such as the Rapid-E.