Soil Fertility Prediction Using Data Mining Techniques

  • Rajat Chaudhari , Saurabh Chaudhari , Atik Shaikh , Ragini Chiloba , Prof.T.D.Khadtare


Agriculture industry in India is the greatest sector for employment but lack of research in this sector is the reason behind less productivity. It's important to implement computational research, Machine learning techniques in Agriculture industry to make India better quality and quantity producer in food sector. Machine Learning techniques are useful in abstracting patterns and establishing relationships between varied data sets and predicting reasonable outputs. It can be efficiently applied in Agriculture industry to improve efficiency in this sector. We have discussed application of Machine learning techniques in Agriculture sector to analyze fertility of soil. Agriculture industry has been always one of the interested areas of research. This study venture to analyze soil data depending upon various factors, classify it and improve efficiency of each model using different combinations. Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively a young research field. The large amounts of data that are nowadays virtually harvested along with the crops have to be analyzed and should be used to their full extent.