Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network

The occurrence and regularity of macrofungal fruitbody formation are influenced by meteorological conditions; however, there is a scarcity of data about the use of machine-learning techniques to estimate their occurrence based on meteorological indicators. Therefore, we employed an artificial neural...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 278 - 21
Main Authors: Somfalvi-Tóth, Katalin, Jócsák, Ildikó, Pál-Fám, Ferenc
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 02.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
631/158
631/449
704/158
704/172
Agaricales
Algorithms
Classification
Fruit bodies
Humanities and Social Sciences
Machine Learning
multidisciplinary
Mycorrhizae
Neural networks
Neural Networks, Computer
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The occurrence and regularity of macrofungal fruitbody formation are influenced by meteorological conditions; however, there is a scarcity of data about the use of machine-learning techniques to estimate their occurrence based on meteorological indicators. Therefore, we employed an artificial neural network (ANN) to forecast fruitbody occurrence in mycorrhizal species of Russula and Amanita , utilizing meteorological factors and validating the accuracy of the forecast of fruitbody formation. Fungal data were collected from two locations in Western Hungary between 2015 and 2020. The ANN was the commonly used algorithm for classification problems: feed-forward multilayer perceptrons with a backpropagation algorithm to estimate the binary (Yes/No) classification of fruitbody appearance in natural and undisturbed forests. The verification indices resulted in two outcomes: however, development is most often studied by genus level, we established a more successful, new model per species. Furthermore, the algorithm is able to successfully estimate fruitbody formations with medium to high accuracy (60–80%). Therefore, this work was the first to reliably utilise the ANN approach of estimating fruitbody occurrence based on meteorological parameters of mycorrhizal specified with an extended vegetation period. These findings can assist in field mycological investigations that utilize sporocarp occurrences to ascertain species abundance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-50638-8