Deep Convolutional Neural Network For Preliminary In-Field Classification Of Lichen Species

Lichens are unique organisms, valued for their pharmacological activity, but also well known as bioindicators of environmental pollution, key determinants for some natural ecological habitats, or just popular elements of decoration. High morphological similarity between lichen species makes their recognition complicated, especially under in-field conditions. Thus, there is a need for a quick and easy method that can help with the preliminary classification of selected lichen species. This paper presents a tool that can facilitate the recognition of Cladonia lichen species, based on a deep convolutional neural network, a model which has nowadays reached a classification level often comparable to humans. The network was trained and tested on twelve Cladonia species using a total of 1164 images, downloaded from various websites. The trained model achieved 60.94% accuracy, which is satisfactory for this novel, but still preliminary, automated classification of lichen species.(c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.