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Dharwad, India

Pujari J.D.,SDMCET | Yakkundimath R.,KLEIT | Byadgi A.S.,University of Agricultural science
Procedia Computer Science | Year: 2015

This paper presents a study on the image processing techniques used to identify and classify fungal disease symptoms affected on different agriculture/horticulture crops. Computers have been used to mechanization, automation, and to develop decision support system for taking strategic decision on the agricultural production and protection research. The plant disease diagnosis is limited by the human visual capabilities because most of the first symptoms are microscopic. As plant health monitoring is still carried out by humans due to the visual nature of the plant monitoring task, computer vision techniques seem to be well adapted. One of the areas considered here is the processing of images of disease affected agriculture/horticulture crops. The quantity and quality of plant products gets reduced by plant diseases. The goal is to detect, to identify, and to accurately quantify the first symptoms of diseases. Plant diseases are caused by bacteria, fungi, virus, nematodes, etc., of which fungi is the main disease causing organism. Focus has been done on the early detection of fungal disease based on the symptoms. © 2015 The Authors.

International Conference on Advanced Computing and Communication Technologies, ACCT | Year: 2013

This paper discusses the various methods used to analyze the texture property of an image. Texture analysis is broadly classified into three categories: Pixel based, local feature based and Region based. Pixel based method uses grey level co occurrence matrices, difference histogram and energy measures and Local Binary Patterns(LBP) Local feature based method uses edges of local features and generalization of co occurrence matrices. Region based method uses region growing and topographic models. © 2013 IEEE.

Rodd S.F.,Gogte Institute of Technology | Kulkarni U.P.,SDMCET | Yardi A.R.,Walchand College
Evolving Systems | Year: 2013

A recent trend in database performance tuning is towards self tuning for some of the important benefits like efficient use of resources, improved performance and low cost of ownership that the auto-tuning offers. Most modern database management systems (DBMS) have introduced several dynamically tunable parameters that enable the implementation of self tuning systems. An appropriate mix of various tuning parameters results in significant performance enhancement either in terms of response time of the queries or the overall throughput. The choice and extent of tuning of the available tuning parameters must be based on the impact of these parameters on the performance and also on the amount and type of workload the DBMS is subjected to. The tedious task of manual tuning and also non-availability of expert database administrators (DBAs), it is desirable to have a self tuning database system that not only relieves the DBA of the tedious task of manual tuning, but it also eliminates the need for an expert DBA. Thus, it reduces the total cost of ownership of the entire software system. A self tuning system also adapts well to the dynamic workload changes and also user loads during peak hours ensuring acceptable application response times. In this paper, a novel technique that combines learning ability of the artificial neural network and the ability of the fuzzy system to deal with imprecise inputs are employed to estimate the extent of tuning required. Furthermore, the estimated values are moderated based on knowledgebase built using experimental findings. The experimental results show significant performance improvement as compared to built in self tuning feature of the DBMS. © 2013 Springer-Verlag Berlin Heidelberg.

Nandibewoor A.,SDMCET | Nandibewoor A.,Bharathiar University | Adiver P.,SDMCET | Hegadi R.,University of Solapur
Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014 | Year: 2015

One of the emerging technologies that can be used to study the rate of vegetation is hyper spectral remote sensing. Hyper spectral satellite image of Western part of Indiana is adopted for our study. This data was further used to calculate different spectral indices. The study on spectral indices which show some significant changes with variation in Vegetation are presented in this paper. These spectral indices are used to monitor the vegetation. The spectral indices that are used are NDVI (normalized differential Vegetation index), SRPI (simple Ratio pigment index), red edge (Clrededge) and SG (VI green). All these spectral indices stated above showed significant changes with change in rate of chlorophyll and nitrogen Concentration. In the graph plotted for different wavelengths verses the reflectance values showed different Curves for change in the area. From this study it can be inferred that the hyper spectral data can also be used to find area containing dense forest, farm lands and bare land. Hence Satellite images can give lot of information that needs to be explored. © 2014 IEEE.

Kulkarni S.B.,SDMCET | Hegadi R.S.,University of Solapur | Kulkarni U.P.,SDMCET
Evolving Systems | Year: 2013

Iris recognition is one of the important authentication mechanisms; authentication needs verification of individuals for uniqueness hence converting iris data into barcode is an appropriate in authenticating individuals to identify uniqueness. Such converted barcode is unique for every iris image. In iris recognition, most applications capture the eye image; extract the iris features and stores into the database in digitized form. The size of the digitized form is equal to or little less than original iris image. This as leads to the drawbacks such as more usage of memory and more time required for searching and matching operations. To overcome these drawbacks we propose an approach wherein we convert extracted iris features into barcodes. This transformation of iris into barcode reduces the space for storage and the time required for searching and matching operations, which are essential features in real time applications. © 2013 Springer-Verlag Berlin Heidelberg.

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