CLASSIFICATION AND CLUSTERING IN YIELD PREDICTION BASED ON SOIL PROPERTIES

Abstract

Data mining in agriculture is becoming a trending subject. Various applications like: pig disease prediction, yield prediction based on rainfall and temperature, assuring quality of apples etc. incorporate the techniques of data mining. Still there is a gap in study for the sole reason of predicting the most common but most important content for the farmer i.e Yield Prediction. Prediction of Yield can be influenced by various factors like: Soil properties, Climate, Seed used and Method of cultivation. In this paper prediction of yield is done by using only the Soil properties of the soil i.e data mining shows that there are surely some patterns in soil properties which constitute to increase or decrease of the production of wheat. The soil properties included for this research include Phosphorous, Potassium (K2O), Electrical conductivity, pH value, Organic carbon and Texture of soil. The Yield prediction was done in two phases. First the pH value was predicted based on the other soil categories and in Second phase Yield was predicted based on the soil properties including predicted pH. Techniques used are classification and clustering with some important algorithms.