Hypericum pruinatum is a medicinal herb containing several bioactive compounds with important pharmacological activity. In this study, we investigated the effects of the salt (0.03 -control, 1, 2.5, 4 and 8 dS m -1 of MgSO4, CaCl2 and NaCl salts) and drought stress (80, 100 and 120% of required water) on the content of phenolic compounds, namely chlorogenic acid, rutin, hyperoside, isoquercetine, quercitrine and quercetine in greenhouse grown plantlets. In general, the salt stress especially in elevating doses increased the levels of all of the compounds analysed, whereas drought stress did not cause a significant chance in chemical content of the plantlets. The present results indicated that abiotic stress factors, particularly salinity, have a marked influence on the content of phenolic constituents in H. pruinatum and it is a salt tolerant species. The results also indicated that phenolic compounds play a significant physiological role in salinity tolerance.

This study was conducted to assess the influence of different salinity and irrigation water treatments on the growth and development of sweet basil (Ocimum basilicum L.). Five salinity levels (0.4, 1.00, 2.50, 4.00 and 8.00 dSm-1) and three different irrigation water regimes (80, 100, 120% of full irrigation) were applied in a factorial design with three replications. Dry root weight, aerial part dry weight and aerial part/root ratio were determined and evaluated as experimental parameters at the end of growing period. Results revealed significant decreases in yields with increasing salinity levels. However, basil managed to survive high salt stress. With increasing salinity levels, decreases in growth were higher in roots than in leaves. Changes in the amount of irrigation water also significantly affected the evaluated parameters.

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Lack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

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