zahra rezghi

Knowledge about soil texture is very important in agricultural studies due to its direct impact on other soil properties. However, determining the soil texture in vast areas requires a lot of time and money. For this reason, researchers are looking for ways to determine this important feature of the soil on a large scale. One of these methods is the use of surface soil spectrometry. In this method, the choice of calibration method significantly affects the accuracy of measuring the characteristics of the surface. In this study, the performance of two regression techniques, namely, partial least-squares regression (PLSR), principal component regression (PCR) were compared to identify the best method to assess sand, silt and clay. For this purpose, 50 soil samples from Tehran province were collected and used as a data set for Calibration and Validation. Soil samples with different moisture levels (oven dry, 5, 10, 15 and 20 w/w) were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The spectra were subjected to three pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), Normalization with SG smoothing (Normal-SG). The R2 results from cross-validation indicated that the PLSR model had a better performance than PCR. Normal + SG pre-processing method for clay loam texture and SG method for sandy clay loam texture showed better estimation of measured properties. The amount of R2 for clay was 0.74, 0.81, 0.97 and 0.87, respectively, in moisture content of oven dry, 5, 15 and 20% in   clay loam texture And 0.95 and 0.61 at oven dry and 5% levels in sandy clay loam. Silt was better predicted by R2 0.67 in moisture content of 5% in clay loam texture and R2 0.97 in moisture content of 20% at sandy clay loam texture. Sand was also predicted (R2= 0.86 and 0.72) in moisture content of 5 and 10% in clay loam texture.

Soil organic matter is considered as an indicator of soil quality and its fertility due to its impact on the physical, chemical and biological properties of soil. The purpose of this study was to investigate the effect of moisture on soil spectral behavior in order to estimate the amount of organic matter using visible-near infrared spectroscopy. Also, the performance of different preprocessing methods was compared in modeling. For this purpose, 50 soil samples from a surface layer (0-30 cm) were randomly collected from some cities in Tehran province. The samples were air-dried and passed through a 2 mm sieve. The amount of organic matter and soil texture were measured in the laboratory. To measure spectral reflectance, soil samples are dry in the oven and they were hydrated with levels of 5, 10, 15 and 20 wt.% (Grams of water / g of soil). Then, Soil samples were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The results of the cross-evaluation indicated that SNV pre-processing method was better in predicting organic carbon content of soil. The best result for the validation group was obtained in dry oven moisture with R2 and RMSE values of 0.83 and 0.422 respectively. Also, R2 and RMSE were 0.75 and 0.543, respectively, at 5% moisture content, 0.70, 0.553 at 10% moisture, 0.605, 0.558 in moisture content of 15%.

As one of the world's major grains, rice has always been given special emphasis. Monitoring agricultural products plays a important role in providing global food supplies. In general, inorder to estimate product output, information such as area and production per unit area is important. Remote sensing technology can provide valuable and timely information on distribution, cropping, and production potential to decision makers in the agricultural sector. Detection by operator with field operation or visual detection cost too much, also associated with human errors. In addition, with regard to the possibility of changing land use, there is always the need for border control of rice paddies. So that the purpose of this study was to separate the rice fields with an automatic method in Gilan province. In recent years remote sensing has been raised as a valuable tool in the management and control of resources and agricultural products. By the same token using satellite images Landsat 8 and soil moisture index (LSWI) and leaves (LAI) and normalized differential vegetation index (NDVI) fully automated method is used to detect the rice paddies. According to this method, then, using the characteristics of the high water range in the early rice transplanting, these areas were identified using specific threshold. Then using another image in the harvest period, unwanted complications such as wetlands and permanent water and wet forests were removed. Final evaluation model with graphical borders of operator were taken from GEO Eye images. The results indicate conformity of 90% of the used method.

More than 50% of patients with hypertension will have end organ damage (such as: CHF, retinopathy, CVA or renal failure).Left ventricular hypertrophy (LVH) is an independent risk factor for death and cardiovascular disease. Both renal dysfunction and LVH are signs of end organ damage so we carried out this study to evaluate the correlation between LVH in echocardiography and kidney function in patients with essential hypertension. This descriptive analytic study was carried out on 102 patients whome reffered to 5 Azar hospital in Gorgan- Iran, suffering from essential hypertension and had proved LVH in echocardiography with no other disease during 2005-6. Kidney and urinary tract assessment such as serum BUN, Creatinine level and kidney sonography was done on subjects. Left ventricular hypertrophy was defined as a left ventricular mass index (LVMI)>=135g/m2 in men and>=110g/m2 in women. Renal function was estimated by means of the serum Creatinine level and proteinuria and sonographic findings of kidney and urinary tract. Data was analysed by Spss-11.5 and Chi-Squre test. The average age of patients were 60±8.8. 58%, 36% and 7% of patients had mild, moderate and sever LVH respectivly. The means of Creatinine and BUN level were 1.09±1.12 mg/dl and 20.6±10.76 mg/dl had direct correlation with kidney dysfunction (P<0.05). There was a significant relationship between LVH severity and presentse of Kidney scar in sonography (P<0.05). This study showed that a very large fraction of the subjects with essential hypertension have kidney problems. Therefore, serum BUN and Creatinine level and sonographic scar of kidney can be reliable markers for assessing and controlling target organ damages that are induced by Essential hypertension.