Dr Amanulla Khan
@mumbai university
Department of Botany
Anjum Islam Janjira Degree College of Science : Murud, Maharashtra , IN
RESEARCH, TEACHING, or OTHER INTERESTS
Plant Science, Horticulture, Soil Science, General Agricultural and Biological Sciences
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Amanulla Khan, K C More, M H Mali, Satish V Deore, and M B Patil
Horizon E-Publishing Group
Ischaemumpilosum (Kleinex Willd.) a weed among the grass is reported for ethno-medicinal practices for treatment of various treatments for human and domestic animals. The current work deals with phytochemical analysis in different parts of plants to find out bioactive compounds. The first-time reported results onI. pilosumreveal the significant phytochemicals by using preliminary phytochemical analysis, UV Visible spectral technique, FTIR analysis and GC-MS analysis. The preliminary phytochemical test confirms the presence of alkaloids, anthraquinone, cardiac glycosides, coumarins, flavonoids, glycosides, phenols, reducing sugars, saponins, steroids, tannin and triterpenes in Ischaemumpilosum.UV Visible spectra and FTIR gives the ranges of absorptions and functional group like Carboxylic acids (O-H) at 2956,92 cm-1, Alkanes (O-H) at 2849,89 cm-1, Aldehydes (C=O) at 1735,92 cm-1, Aromatic Rings (C=C) at 1462,95 cm-1, Alkanes (C-H) at 1377,97 cm-1, Esters (C-O) 1166,95 cm-1and Phenyl Ring (C-H) 758,97 cm-1. The GC-MS analysis related twenty-one compounds like Phenol, 4-bis (1,1-dimethylethyl), Pentanoic acid, 5-hydroxy, 2,4-di-t-butylphenyl esters, E-15-Heptadecenal, 1-Hexadecanol, n-Hexadecanoic acid, l (+)-Ascorbic acid 2,6-dihexadecanoate, Palmitic anhydride, Cycloeicosane, Cis-13-Octadecenoic acid and Triacontane from Ischaemumpilosumleaves extract.
U. Archana, Amanulla Khan, Appani Sudarshanam, C. Sathya, Ashok Kumar Koshariya, and R. Krishnamoorthy
IEEE
Deep learning and computer vision have recently emerged as useful methods for the phenotyping of sick plant tissue. The majority of prior research focused on illness categorization based on images. In conventional agricultural procedures, the diagnosis of illnesses affecting rice plants is performed by professionals in a manner that is very subjective, while laboratory testing takes a significant amount of time. As a direct result of this, there is a decrease in agricultural productivity, which results in economic loss for farmers. In order to find a solution to this problem, there is a pressing need to create methods that are quick and accurate in identifying and categorizing illnesses that affect rice plants. In the field of agriculture, the development of image-based automated systems for the categorization of rice plant diseases has become an intriguing and expanding study subject. When it comes to classifying rice plant diseases, color is one of the most crucial factors. Within the scope of this investigation, an image-based method is provided for classifying rice plant diseases based on color characteristics. Deep convolutional neural systems consume recently realized astonishing results in a number of applications, one of which is the classification of tomato plants that have been affected with many illnesses. Deep convolutional neural networks with a variety of residual networks underpin our work. In conclusion, this research study has conducted disease classification based on tomato leaves by employing a pre-trained deep CNN in conjunction with the residual network. The result that ResNet-50 produced demonstrates a remarkable result with an accuracy of 96.35%
Nasir Aziz Wagay, Shah Rafiq, Amanulla Khan, Zahoor Ahmad Kaloo, Abdul Rashid Malik, and P. V. Pulate
Springer Nature Singapore