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On the benefits of using a high-resolution mesoscale model to improve wind field for the study of upwelling off the Indian coasts

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International Journal of Digital Earth 2009年第2期2卷 122-133页

作者： kailasam muni krishna Department of Meteorology and Oceanography Andhra UniversityVisakhapatnam-530003India

Upwelling favourable winds along the Indian coasts were studied using a highresolution(9 km grid)atmospheric mesoscale model(MM5)reanalysis from 1 to 5 June 2006,off the west coast of India and from 5 to 10 December 2005,off the east coast of *** winds are verified with satellite winds at two *** wind hind-casts from the above periods reveal narrow bands(about 10×50 km)of strong winds to the coast and adjacent to the major coastal *** bands,which are sub-grid scale in operational models,were capable of generating local upwelling greater than 10 m/*** the mean and spatial components of the model alongshore wind increase by a factor of 3 to 4 from the south to the north along the west coast,and by a factor of 2 to 3 from the north to the south along the east coast of *** field increases will drive enhanced ocean upwelling along the west coast of India,and the new hypothesis suggests that the variations in local wind may contribute to the coastal upwelling in this region.

关键词： coastal upwelling QuikSCAT satellite wind variability AVHRR MM5

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View on Bay of Bengal upwelling area on the basis of 19-years of satellite sea surface temperature

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International Journal of Digital Earth 2008年第3期1卷 304-314页

作者： K.muni krishna Department of Meteorology and Oceanography Andhra University Visakhapatnam-530003India

The seasonal and interannual variation of upwelling along the east coast of India between 68N and 228N was studied for the period 19852003 using NOAAAVHRR sea surface temperature *** seasonal migration of pronounced upwelling,which follows the seasonal shift of the winds in transition period and northeast monsoon,was *** temporal mean sea surface temperature images clearly show the upwelling season,as does the seasonal sea surface temperature *** dominant features of the upwelling system are also the most variable,with most of the variance being explained by the seasonal ***-cyclic behaviour of sea surface temperature on interannual scales has also been observed.

关键词： AVHRR sea surface temperature seasonal interannual

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An IoT-based agriculture maintenance using pervasive computing with machine learning technique

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 201-214页

作者： Swathi kailasam Sampath Dakshina Murthy Achanta P.Rama Koteswara Rao Ramesh Vatambeti Saikumar Kayam CSE Koneru Lakshmaiah Education FoundationGunturIndia ECE Vignan’s Institute of Information TechnologyVisakhapatnamIndia ECE NRI Institute of TechnologyKrishnaIndia CHRIST(Deemed to be University) BengaluruIndia ECE Koneru Lakshmaiah Education FoundationGunturIndia

Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains *** significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization *** this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and *** the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc ***/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is *** proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less *** now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced ***,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this *** implemented design is verified on Python 3.7.8 software for simulation ***-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop *** of this,a disease-finding system has been *** methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are ***/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.

关键词： Crop harvesting Detection of plant disease RFO Classification Threshold segmentation

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