PHYTOCHEMICAL STUDIES AND QUALITATIVE ANALYSIS BY TLC OF MURRAYA KOENIGII BARK EXTRACT

Murraya koenigii is a medium size, ever green plant which has been utilized as a source of food, medicine, and other agricultural purposes in different communities. Thus, the preliminary phytochemical analysis and TLC separation was done using methanol, n-hexane, and ethyl acetate(1:3:1), as solvent system while iodine vapour as spotting agent. The phytochemical screening of diethyl ether extracts of bark revealed the presence of carbohydrates , anthraquinones glycosides ,saponins ,flavanoids, and alkaloids, while chloroform extracts of bark revealed carbohydrates, tannins, saponins, and alkaloids, while acetone extracts of bark revealed the presence of carbohydrates, anthraquinones glycosides, flavanoids and alkaloids,while ethanol extracts of bark revealed the presence of carbohydrates, tannins, anthraquinones glycosides,s aponins, flavanoids and alkaloids.TLC separation showed (3) spots each of Diethyl Ether, Chloroform, Acetone, Ethanol from bark extracts. From our findings, it can be concluded that Murraya Koenigii contains some significant phytochemicals that can exhibit desired therapeutic activities such as Antioxidant, Anti-Microbial, Anti-Fungal, Anti-Diabetic, Anti-Ulcer and Cosmetic use. However, there is a need to conduct further Pharmaceutical Analysis on test extracts in order to establish these biomedical applications. Keywords: Thin Layer Chromatography, Murraya koenigii Bark, Phytochemical screening

Identification of Water-Stressed Area in Maize Crop Using Uav Based Remote Sensing

Agronomic inputs such as water , nutrients and fertilisers play a vital role in the health, growth and yield of crops. The lack of each of these inputs induces biotic and abiotic stress in the crop. Farmers are relying on groundwater because of decreased rainfall. The irrigation method can be improved by acquiring awareness of the health of crops and soils. In general, crop and soil quality is controlled by means of manual observation, which is time-consuming, labour-intensive and contributes to incorrect choices and substantial waste of resources. There is also an immediate need to automate the inspection process that will finally benefit farmers and agricultural scientists. In this paper, the identification of the water-stressed areas in the crop(maize) field has been studied, and an Unmanned Aerial Vehicle (UAV) based remote sensing is used to automate the crop health-monitoring process. We proposed a framework (model) based on Convolutional Neural Networks (CNN) to identify the stressed and normal/healthy areas in the maize crop field. The performance of the proposed framework has been compared with different models of CNN, such as ResNet50, VGG-19, and Inception-v3. The results show that the proposed model outperforms the baseline models and successfully classify stressed and normal areas with 95 % accuracy on train data and 93 % accuracy with 0.9370 precision and 0.9403 F1 score on test data