Dyanamics of Experimental Observation and Estimation of Soil Errosion using Agricultural Data Sciences


  • Sangayya Gulledmath, Arul Kumar V


Data Mining is conventionally and conveniently greater domain for various aspects in commemorate with knowledge extraction.  In the dynamic sense participative of Data Mining is not only extraction it also helps for creating knowledge as prediction which helps to create inner intelligence for decision making. Overall this area of computer science is in demand because of its application in various adoptive levels of humans. We move further to think how generously this can be used to find soil erosion to protect and preserve. As on date many people are working in and around of crop yield or soil properties, finding patterns of soil deceases, method of cultivation, Land classification and survey these are only  few. One of the major contributions is working with machine learning algorithms for finding the contributory factor of soil erosion and rate of soil erosion can be classified and treated with soil erosion management techniques. Soil is most demanding and needy substance of human life on the earth. We use technology but forget the farmers to empower them with modern tools for agriculture. As various applications of research scholars has coined the word use of Data Mining aspects in Agriculture known as Agricultural Data Mining or comprehension of broader umbrella we termed as data science. The paper deals with soil erosion and protecting soil surface by considering selected areas of Bangalore rural and deficiency prediction based on natural movement of soil erosion using efficient data mining algorithms.