Mushroom genera determination using machine learning
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Mushroom determination using classification manuals is a tedious and time-consuming task for mycologists and mushroom hunters. Machine learning provides a tool to automate this process based on mushroom images using a small dataset. Since mushroom genera level classification has been understudied, it is important to direct attention to this matter. In this study, advanced machine learning algorithms were used in order to classify Cantharellus, Coprinus, Pholiota and Russula mushroom genera that are widely spread in Estonia. The classification was done based on the image grayscale pixels. To improve the classification accuracy, majority voting and mean rule methods from the ensemble-based classification were applied to the dataset. The highest accuracy obtained was 75.38%, with the majority voting method fusing five high performing classifiers. This study showed that ensemble methods improve the mushroom genera classification accuracy compared to individual classifiers. In addition to a novel approach of classifying mushrooms on the level of genera, a new labelled mushroom image dataset was collected that can be used in the future for similar studies.
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