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Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach
BACKGROUND: Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aim...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456011/ https://www.ncbi.nlm.nih.gov/pubmed/32861248 http://dx.doi.org/10.1186/s12898-020-00316-4 |
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author | Saffariha, Maryam Jahani, Ali Potter, Daniel |
author_facet | Saffariha, Maryam Jahani, Ali Potter, Daniel |
author_sort | Saffariha, Maryam |
collection | PubMed |
description | BACKGROUND: Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, −2, −4, −6, −8, −10 and −12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development. RESULTS: Comparing to the MLR, the MLP model represents the significant value of R(2) in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds. CONCLUSIONS: Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands. |
format | Online Article Text |
id | pubmed-7456011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74560112020-08-31 Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach Saffariha, Maryam Jahani, Ali Potter, Daniel BMC Ecol Research Article BACKGROUND: Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, −2, −4, −6, −8, −10 and −12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development. RESULTS: Comparing to the MLR, the MLP model represents the significant value of R(2) in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds. CONCLUSIONS: Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands. BioMed Central 2020-08-29 /pmc/articles/PMC7456011/ /pubmed/32861248 http://dx.doi.org/10.1186/s12898-020-00316-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Saffariha, Maryam Jahani, Ali Potter, Daniel Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
title | Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
title_full | Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
title_fullStr | Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
title_full_unstemmed | Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
title_short | Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
title_sort | seed germination prediction of salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456011/ https://www.ncbi.nlm.nih.gov/pubmed/32861248 http://dx.doi.org/10.1186/s12898-020-00316-4 |
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