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Effect of Mycorrhizal Inoculation and Fertilization with Plant Residues on the Growth of Chard Plant
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In order to study the effect of inoculation with mycorrhiza and fertilization with plant residues on the growth of plants, we used two factors: the first two levels of mycorrhiza inoculation, Glumus mossea (0 and 10 g.pot-1) and the second factor, four levels of plant residues (10 g.pot-1) celery plant residues, 10 g pot-1 mint residues, and 10 g pot-1 black bean seed residues. Mychorrizal treatment (10 g pot-1) increased the number of mycorrhiza spores and the infection percentage of mycorrhizal by 917.44% and 13088.23%, respectively; celery treatment (10 g.pot-1) increased the chlorophyll index in the leaves and height of the chard plant by 31.34% and 94.04%, respectively; and black seed treatment (10 g.pot-1) increased the percentage of dry matter in the leaves and the percentage of carbohydrates in the leaves by 81.51% and 53.36%, respectively.The results showed the bilateral interactions between the experimental factors that the treatment of mycorrhizal inoculation exceeded (10 g pot-1) and celery (10 g.pot-1) residues in most of the study parameters in each of the Total Chlorophyll index in the leaves (SPAD), plant height, percentage of dry matter in the leaves (%), percentage of carbohydrates in the leaves, number of mycorrhiza spores, and infection percentage of mycorrhizal were (46%, 150.89%, 139.88%, 92.07%, 3283.45%, and 4000%, respectively, compared to the control treatment.

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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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