A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)

The management of plant diseases is vital for the economical production of food and poses important challenges to the employment of soil, water, fuel and alternative inputs for agricultural functions. In each natural and cultivated populations, plants have inherent sickness tolerance, however there also are reports of devastating impacts of plant diseases. The management of diseases, however, within reason effective for many crops. sickness management is allotted through the employment of plants that square measure bred permanently resistance to several diseases and thru approaches to plant cultivation, like crop rotation, the employment of pathogen-free seeds, the given planting date and plant density, field wetness management, and therefore the use of pesticides. so as to enhance sickness management and to stay up with changes within the impact of diseases iatrogenic by the continued evolution and movement of plant pathogens and by changes in agricultural practices, continued progress within the science of soil science is required. Plant diseases cause tremendous economic losses for farmers globally. it's calculable that in additional developed settings across massive regions and lots of crop species, diseases usually cut back plant yields by ten percent per annum, however yield loss for diseases usually exceeds twenty percent in less developed settings. Around twenty-five percent of crop losses square measure caused by pests and diseases, the Food and Agriculture Organization estimates. to unravel this, new strategies for early detection of diseases and pests square measure required, like novel sensors that sight plant odours and spectrographic analysis and bio photonics that may diagnose plant health and metabolism. In artificial neural networks, deep learning is an element of a broader family of machine learning approaches supported realistic learning. Learning is often controlled, semi-supervised or unmonitored. to handle several real-world queries, Deep Learning Approaches are normally used. so as to differentiate pictures and acknowledge their options, coevolutionary neural networks have had a larger result. This article will do a Leaf Disease Identification Survey with Deep Learning Methods. It takes Sugarcane leaf as an instance to our paper

Anti diabetic effect of ethanolic extract of leaves of Ocimum sanctum in alloxan induced diabetes in rats

Background: Diabetes mellitus refers to a group of common metabolic disorders that share the phenotype of hyperglycemia resulting from defects of reduced insulin secretion, decreased glucose utilization and increase in glucose production. It is estimated that there are currently 285 million people worldwide and this number is set to increase to 438 million by the year 2030. India has the highest number of patients with known diabetes worldwide, with a prevalence of 11.6%. The aim of the study was to evaluate the anti diabetic activity of ethanolic extract of leaves of plant Ocimum sanctum in alloxan induced diabetes in rats.Methods: The study was conducted on 4 groups of 6 rats each to evaluate the hypoglycaemic effect of ethanolic extract of Ocimum sanctum. Glibenclamide was used as a standard drug and the results were compared in reference to it. Tween 80 was given for both normal and diabetic control groups. The fasting blood sugar levels were recorded on 1st, 3rd, 5th, 7th, 10th days by glucometer.Results: The results indicate that the test compound ethanolic extract of Ocimum sanctum has significant and sustained oral hypoglycaemic activity, comparable with the hypoglycaemic effect of glibenclamide, a sulfonylurea.Conclusion: The hypoglycaemic potential of the test compound is found to be comparable with that of the standard drug glibenclamide

Processing Real World Datasets using Big Data Hadoop Tools

631-635Today’s digital world computations are extremely difficult and always demands essential requirements to significantly process and store an enormous size of datasets. Therefore, this is mostly structured, semi-structured and unstructured generated data with more velocity at beyond the limits and double day by day from a wide variety of applications. In the last decade, many organizations have been facing major problems in handling and processing massive chunks of data, which could not be processed efficiently due to the lack of enhancements on existing technologies. This paper, introduce advanced data processing tools to solve the extreme problems as efficiently by using the most recent and world’s primary powerful Map-Reduce framework, but it has few data processing issues. Therefore, recently Apache Spark fastest tool has introduced to overcome the limitations of Map Reduce

Theoretical study of a 16-µm CO2 downstream-mixing gasdynamic laser: A two-dimensional approach

A 16-µm CO2-N2 downstream-mixing gasdynamic laser, where a cold CO2 stream is mixed with a vibrationally excited N2 stream at the exit of the nozzle, is studied theoretically. The flow field is analyzed using a two-dimensional, unsteady, laminar and viscous flow model including appropriate finite-rate vibrational kinetic equations. The analysis showed that local small-signal gain up to 21.75 m−1 can be obtained for a N2 reservoir temperature of 2000 K and a velocity ratio of 1:1 between the CO2 and N2 mixing streams. Applied Physics Letters is copyrighted by The American Institute of Physics