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 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.

Summary: Basma tobaccos, in addition to Izmir and Samsun type tobaccos, are the most important high-quality oriental tobacco types grown in Turkey. This research was carried out to determine the effects of a variety of environmental conditions, in four locations on different altitudes on the yield as well as on nicotine, sugar and phenolic substances content. The plant material of the study included 21 Basma lines, which were selected according to their morphological differences and from genotypes separated by DNA fingerprint analysis, and four standard cultivars/lines (checks). The quality grade index of the genotypes was determined by the American grading method and chemical analyses were carried out using a high-performance liquid chromatography (HPLC) system. All the parameters investigated indicated that genotypes were significantly affected by the variation in environmental conditions. Organoleptic observations showed that the quality grade index of genotypes ranged from 24.17 to 100%, and the ERB-7, ERB-13, ERB-15, ERB-19 and ERB-38 lines had the best quality. Nicotine contents of tobacco lines were between 0.31 and 3.15% dry matter (DM). Glucose, fructose and their sum (reducing sugar) contents of genotypes ranged from 1.16 to 8.88% DM, from 2.60 to 8.66% DM and from 4.44 to 15.03% DM, respectively. The ERB-21 and ERB-30 lines are noteworthy tobacco types in terms of reducing sugar contents. The values of chlorogenic acid, one of the phenolic compounds, ranged from 40.67 to 1119.76 ppm, the values of rutin from 121.05 to 1021.53 ppm, and the sum of these two phenolic compounds was from 174.94 ppm to 2019.41 ppm. The effect of variations in the environment on the quality of Oriental tobacco can be clearly explained by the variations in the parameters.

The artificial neural network (ANN) method was used in this study for predicting sugar beet (Beta vulgaris L.) leaf chlorophyll concentration from leaves. The experiment was carried out in field conditions in 2015-2016. In this research, symbiotic mychorrhizae as Bio-one (Azotobacter vinelandii and Clostridium pasteurianum) in commercial preparation (10 kg/da) and ammonium sulfate (40 kg/da) were use used as a fertilizer. In order to measure the leaves' chlorophyll concentration we used a SPAD-502 chlorophyll meter. Artificial neural network, red, green, and blue components of the images were used which was developed to predict chlorophyll concentration. The results showed the ANN model able to estimate sugar beet leaf chlorophyll concentration. The coefficient of determination (R-2) was found to be 0.98 while mean square error (MSE) was obtained as 0.007 from validation.