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bsj-2164
Effect of different concentration of Gibberellic and Proline acid in the growth and production of pea plant Pisum sativum L.
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This experiment was conducted to evaluate the effect of foliar spraying with gibberellic acid concentrations (0,50,100)mg.L­¹ and proline acid concentrations (0, 25, 50)mg.L­¹ and their interactions on some growth parameters of pea plant using clay pots in the botanical garden of Biology Department College of Education for pure science Ibn –Al-Haitham ,Baghdad University, for the growing season 2012-2013 the experiment involved the studing of some growth parameters as plant?s height, dry weight, wt. of pods.plant­¹, biology yield and the concentration of some major elements (nitrogen, phosphorus and calcium) in plant?s seeds. The experiment was designed according to Completely Randomized Desig(CRD) with three replications. Results revealed that foliar application with the concentrations of both acid caused a significant increase in the growth parameters, and the interaction gave a significant effect, the concentration 100 mg.L­¹ gibberellic acid and the concentration 50 mg.L­¹ proline gave the best value for plant?s height, dry weight, the concentration of nitrogen and calcium in plant?s seeds, but the concentration 100 mg.L­¹ gibberellic acid and the concentration 25 mg. L­¹ proline acid gave the higheat value for the concentration of phosphorus in plant?s seeds while the concentration 50 mg .L­¹ gibberellic acid and the concentration 25 mg.L­¹ proline gave the best values for wt. of pods.plant­¹, biology yield.

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Publication Date
Sun Feb 27 2022
Journal Name
Iraqi Journal Of Science
Plants Leaf Diseases Detection Using Deep Learning
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     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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