Oils were extracted from seeds of Trigonella foenum graecum as well as from seeds of Elettaria cardamomum, then separated and characterized by Gas Chromatography with Mass spectra GC/MC twelve different compounds of Trigonella foenum graecum oil were identified, the highest rate was for the compound 9, 12-Octadecadienoic acid (Z,Z) which was found with rate of 38.97
،%then accompanied with 31.85% of 9-Octadecenoic acid (Z)-, methyl ester.
On other hand fifty four (54) different compounds were separated from fixed oils of Elettaria cardamomum , the highest rate for the compound 3-Cyclohexene-1-methanol, .alpha.,.alpha.,4-trimethyl-, acetate which was 22.88% then the compound Butyl 9,12-octadecadienoate with a rate of 21.22 % .
The lethal effect of oils against Tribolium castaneum was studied for several periods, the results clarify that the oils of Trigonella foenum graecum and Elettaria cardamomum , significant effects on insect mortality reached to (96.9 and 92.5 )% when they were used with concentration 700 ppm after72 hours of treatment.
The reaction oisolated and characterized by elemental analysis (C,H,N) , 1H-NMR, mass spectra and Fourier transform (Ft-IR). The reaction of the (L-AZD) with: [VO(II), Cr(III), Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Hg(II)], has been investigated and was isolated as tri nuclear cluster and characterized by: Ft-IR, U. v- Visible, electrical conductivity, magnetic susceptibilities at 25 Co, atomic absorption and molar ratio. Spectroscopic evidence showed that the binding of metal ions were through azide and carbonyl moieties resulting in a six- coordinating metal ions in [Cr (III), Mn (II), Co (II) and Ni (II)]. The Vo (II), Cu (II), Zn (II), Cd (II) and Hg (II) were coordinated through azide group only forming square pyramidal
... Show MoreAgriculture 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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