International Journal of Advanced Networking and Applications

S. Senthil ¹, C. K. Lokesh ¹, D. Sai Yashwanth ¹

Affiliations
1 School of CSA, REVA University, IN

Abstract

India is one of the well-known countries in world, in the area of pharmacy specially food horticulture. India produces nearly 5.00 lakh tones/annum of pomegranate. Fruit gradation is one of the most vital parts in fruit horticulture. The project design presented by this paper is from same problematic area. In our project design we developed systems which classify diseases affecting pomegranates using K-means clustering and SVM techniques and routing algorithm. Now a days the disease Bacterial Blight which is caused by "Xanthomonas Axonopodis PV. Punicae" is growing rapidly day by day in pomegranate cultivation. This is fungal bacteria and caused by many parameters like environment, air, humidity, temperature. It causes heavy losses in production quality and quantity each year, especially in climates with rainfall and high temperature. Cercospora fruit spot is caused by the fungus and the full name of this fungal disease is “Pseudocercospora Angolensis”. Leaves of affected plants will produce circular spots with light brown to grayish centers. We are classifying the different pomegranate variety in accordance with their diseases. This paper deals with pomegranate grading and identification of disease system with judging parameters. The specialty of design is it creates a model which helps to decide appropriate criteria for healthy fruit. This project design is acts as advance system model in Indian horticulture for deciding ranges of mean, variance, entropy values by which the quality of fruit is decided. These parameters are judging parameters of our project design.

Keywords

K-Means, SVM (Support Vector Machine), Mean, Variance, Imageprocessing, Bacterial Blight.

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P. V. Varish ¹, C. K. Lokesh ¹, Kavitha ¹

Affiliations
1 School of CSA, REVA University, IN

Abstract

Web mining is used to find the different patterns in data by various categories like web usage mining, web structure mining and web content mining. The method used to gather data by web spiders and web search engines are known as web content mining. The formation of a website can be tartan by using web structure mining and we can test the data of a user’s browser by using web usage mining. The web content mining is a second phase of web mining, which deals with extraction of images, graphs, text etc. The spotlight of this work is to present a brief survey on different techniques used in web content mining. We presented a brief review of different web content mining approaches like multimedia mining, unstructured mining, structured mining and semi-structured mining.

Keywords

Web Mining, Web Content Mining, Multimedia Mining, Web Crawlers, Summarization, Information Extraction.

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