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.
The Interaction Effect of Urea and Super Phosphate Fertilizer on the Nutritional Status of Wheat Plant (Triticum aestivum L.)
wheat plant
urea fertilizer
super phosphate fertilizer
Nutritional status
Publication Date
Sun Feb 27 2022
Journal Name
Iraqi Journal Of Science
Plants Leaf Diseases Detection Using Deep Learning
Plant Leaf Disease
Leaf Disease Detection and Classification
Deep Learning
Convolution Neural Network
Detection Accuracy
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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