A pots experiment was conducted in the plastic house of Kalar Horticulture station Province of Sulaimania, by using soil brought from one of Kalar region fields during growing season of 2007-2008, to study the effect of increasing levels of urea fertilizer which is (0, 0.20,0.40,0.80) g/pot which equals to (0,100,200,400) kg urea/ha, and super phosphate fertilizer which is (0,0.24,0.48)gm/pot which is equal (0,120,240) kg sup/ha, in nutrition state of wheat IPA 95 component, clay determining Nitrogen, Phosphor and Potassium content in green part and seeds. The Completely Randomized Design was used with three replication per treatment combination. Results showed increasing content nuteriant elements in green part and seed with increasing level of the studied fertilizers.
Background: Post-partum depression (PPD) is a form of postnatal depression that affects mothers. Clinical manifestations usually appear within six months after delivery. Risk factors that influence the severity of post-partum depression are not fully known in the Iraqi population.
Objectives: We aim to evaluate the risk factors and identify potential predictors that may influence the symptom levels (severity) of post-partum depression among Iraqi women from Baghdad.
Subjects and Methods: The current study is cross-sectional, and we used the Edinburgh Postnatal Depression Scale (EPDS) and a cut-off value of 13 to differentiate patients into two those with lower symptom levels (LSL) and higher symptom levels (HSL). We also explored p
Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes. The data augmentation techniques have been used. In addition to dropout and weight reg
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