A laboratory experiment studied the effects of the green tea (Camellia sinensis L.) aqueous extract at concentrations of 10, 20, and 30 ppm on the germination and growth traits of the mung bean (Vigna radiata L.), carried out in 2021 at the Department of Biology, College of Education for Pure Sciences, Ibn Al-Haitham, University of Baghdad, Iraq. The results showed that Camellia sinensis green tea extracts played a vital role by significantly boosting all the examined characteristics compared with the control treatment. The aqueous extract of Green tea at concentrations of 10 and 20 ppm gave the best performance in increasing germination rates, germination speed, plant promoter indicator, and seedling strength compared with the control treatment. Meanwhile, the concentration of 30 ppm indicated a significant increase in the shoot length, root system, carbohydrates, proteins, proline, superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) compared with other concentrations used and control treatment
EFFECT OF SEED PRIMING WITH GREEN TEA (CAMELLIA SINENSIS L.)
AQUEOUS EXTRACT ON GERMINATION AND GROWTH TRAITS OF MUNG BEAN (VIGNA RADIATA L.)
Mung bean (Vigna radiata L.)
green tea (Camellia sinensis L.) extract
germination
growth traits
antioxidant enzyme
superoxide dismutase
peroxidase
catalase
carbohydrates
proteins
proline
Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Using K-mean Clustering to Classify the Kidney Images
Computed Tomography (C.T.)
K-Mean (K.M.)
Image Classification
Gray-Level Co-occurrence Matrices (GLCM)
Red Green Blue (RGB)
This study has applied digital image processing on three-dimensional C.T. images to detect and diagnose kidney diseases. Medical images of different cases of kidney diseases were compared with those of healthy cases. Four different kidneys disorders, such as stones, tumors (cancer), cysts, and renal fibrosis were considered in additional to healthy tissues. This method helps in differentiating between the healthy and diseased kidney tissues. It can detect tumors in its very early stages, before they grow large enough to be seen by the human eye. The method used for segmentation and texture analysis was the k-means with co-occurrence matrix. The k-means separates the healthy classes and the tumor classes, and the affected
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