Deep convolutional neural network for preliminary in-field classification of lichen species. (April 2021)

• Record Type: Journal Article
• Authors: Galanty, Agnieszka; Danel, Tomasz; Węgrzyn, Michał; Podolak, Irma; Podolak, Igor
• Is Part Of: Biosystems engineering. Volume 204(2021)
• Page Start: 15
• Page End: 25
• Publication Date: 2021-04
• Subjects: neural network -- species recognition -- lichen -- Cladonia -- in-field classification; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2021.01.004
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : 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. Highlights: Deep convolutional neural network was designed to determine the lichen species. The model was trained and tested on images of 12 Cladonia species, on1164 images. Satisfactory 60.94% accuracy was achieved for the model. This is the first use of neural network to identify lichen species. The model can be useful for lichenologist and non-expert nature amateurs.