Paddy Yield Predictor Using Temperature, Rainfall, Soil pH, and Nitrogen

Agriculture is the backbone of India which indirectly contributes for an Indian economy. Farmers who are the drivers of agriculture are facing lot of problems for proper identification of the crops that can be cultivated for the specific soil conditions and to maximize the crops yield. All these problems are due to lack of technology and scientific techniques being used in agriculture. Crop yield varies as a result of variations in atmospheric and soil conditions. Data mining mainly focuses on methods to elicit useful knowledge from the dataset. There are several data mining approaches that can be used for the purpose of predicting crops yield and finding association among attributes contributing for the crops yield. This paper mainly intensifies on various association algorithms, namely Apriori, Eclat, and AprioriTid to find the association among temperature, rainfall, soil pH, soil nitrogen, and paddy yield.