Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach

• Published in: BMC ecology 2020-08, Vol.20 (1), p.48-48, Article 48
• Main Authors: Saffariha, Maryam, Jahani, Ali, Potter, Daniel
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Agricultural management; Artificial Intelligence; Datasets; Decision support systems; Drought; Ecological stress; Ecosystem; Ecosystems; Geographical distribution; Germination; Graphical user interface; Lamiaceae; Mathematical functions; Modelling; Moisture content; Multi-layer perceptron; Multilayer perceptrons; Multiple linear regression; Neural network; Neural networks; pH effects; Protected areas; Range management; Rangelands; Regression analysis; Salinity; Salinity effects; Salvia; Salvia limbata; Seed germination; Seeds; Sensitivity analysis; Sodium chloride; Soil moisture; Soil salinity; Strategic management; Stresses; Temperature; Variables; Water content
• ISSN: 1472-6785; 1472-6785
• DOI: 10.1186/s12898-020-00316-4

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. Comparing to the MLR, the MLP model represents the significant value of R 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. 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.