Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network
Abstract 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 artifici...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50638-8 |
| _version_ | 1797363459233415168 |
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| author | Katalin Somfalvi-Tóth Ildikó Jócsák Ferenc Pál-Fám |
| author_facet | Katalin Somfalvi-Tóth Ildikó Jócsák Ferenc Pál-Fám |
| author_sort | Katalin Somfalvi-Tóth |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T16:21:40Z |
| format | Article |
| id | doaj.art-a52ad3b467344731a2896f9e01092068 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T16:21:40Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a52ad3b467344731a2896f9e010920682024-01-07T12:19:45ZengNature PortfolioScientific Reports2045-23222024-01-0114112110.1038/s41598-023-50638-8Verification study on how macrofungal fruitbody formation can be predicted by artificial neural networkKatalin Somfalvi-Tóth0Ildikó Jócsák1Ferenc Pál-Fám2Department of Agronomy, Institute of Agronomy, Hungarian University of Agriculture and Life SciencesDepartment of Agronomy, Institute of Agronomy, Hungarian University of Agriculture and Life SciencesDepartment of Agronomy, Institute of Agronomy, Hungarian University of Agriculture and Life SciencesAbstract 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.https://doi.org/10.1038/s41598-023-50638-8 |
| spellingShingle | Katalin Somfalvi-Tóth Ildikó Jócsák Ferenc Pál-Fám Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network Scientific Reports |
| title | Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network |
| title_full | Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network |
| title_fullStr | Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network |
| title_full_unstemmed | Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network |
| title_short | Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network |
| title_sort | verification study on how macrofungal fruitbody formation can be predicted by artificial neural network |
| url | https://doi.org/10.1038/s41598-023-50638-8 |
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