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 R2 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.

中文翻译：

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保护区生态胁迫下丹参种子的萌发预测：一种人工智能建模方法。

丹参是唇形科的一个大的，多样的，多态的属，包括约900种观赏植物，在世界范围内几乎是世界性分布的药用物种。鼠尾草种子萌发的成功取决于许多生态因素和压力。我们旨在利用人工智能建模技术（例如MLR（多重线性回归）和MLP（多层感知器））在盐度，干旱，温度和pH值的四个生态压力下分析鼠尾草种子的萌发。在非生物条件的不同组合中测试了S.limbata种子的发芽率。五个不同温度分别为10、15、20、25和30°C，七个干旱处理分别为0，-2，-4，-6，-8，-10和-12 bar，八个盐度处理包含0、50， 100.150、200、250、300和350 mM的氯化钠，测试了4、5、6、7、8和9的6种pH处理。实际上，已经测试了228种组合以确定模型开发的发芽百分比。与MLR相比，MLP模型代表R2在训练（0.95），验证（0.92）和测试数据集（0.93）中的显着价值。根据敏感性分析的结果，干旱，盐度，pH和温度的值分别被认为是影响唇缘链球菌种子萌发的最显着变量。土壤中水分含量高和盐分低的区域具有很高的潜力，使边缘链球菌种子发芽。另外，建议使用温度为18.3°C，pH为7.7，以达到最大数量的发芽链霉菌种子的数量。多层感知器模型可帮助管理人员确定S的成功。在农业或自然生态系统中种植唇缘种子。设计的图形用户界面是农业或牧场管理员的环境决策支持系统工具，用于预测在不同土地生态约束下S.limbata种子发芽的成功率（百分比）。