ArticleBMC ecology2020
Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach.
Article in BMC ecology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 43 citations in OpenAlex.
- Wheat seed germination prediction in response to temperature, water potential, and salinity using an artificial neural network.Scientific reports · 2026Article
- Development of an efficient in vitro micropropagation and biochemical profiling of Salvia halophila, an endemic Turkish sage.BMC plant biology · 2026Article
- Simulation of emitter discharge along drip laterals under drip fertigation system using artificial neural network.PloS one · 2025Article
- Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables.Stochastic environmental research and risk assessment : research journal · 2022Article
- A comparison of artificial intelligence techniques for predicting hyperforin content inPlant direct · 2021Article
- Prediction of hypericin content in Hypericum perforatum L. in different ecological habitat using artificial neural networks.Plant methods · 2021Article
- Artificial intelligence accuracy assessment in NOScientific reports · 2021Article
- Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques.Scientific reports · 2021Article
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSalvia 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.
resultsComparing to the MLR, the MLP model represents the significant value of R
conclusionsMultilayer 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.
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