28-01-2013, 12:23 PM
A CROP-WEATHER MODEL FOR PREDICTION OF PADDY-YIELD USING DATAMINING TECHNIQUE
1A CROP-WEATHER.docx (Size: 14.42 KB / Downloads: 27)
Abstract
Agriculture is a business with risk. Crop production depends on climatic, geographical, biological, political and economic factors. Because of these factors there are some risks, which can be quantified when applied appropriate regression methodologies. Actually accurate information about the nature of historical yield of crop is important modeling input, which are helpful to farmers & Government organization for decision making process in establishing proper policies. The advances in computing and information storage hove provided vast atmost of data. The challenge has been to extract knowledge from this raw data; this has lead to new methods and techniques such as data mining that can bridge the knowledge of the data to the crop yield estimation. This research aimed to access these new data mining technique and apply them to the various variables consisting in the database to establish if meaningful relationships can be found.
As, Rice is the staple food in many countries and is grown in varied climates from per-humid to semi-arid areas. Crop–weather models were used to predict rice yield in India. However, in spite of a significant influence of solar radiation on rice yield, none of these models used solar radiation as one of the predictors. One of the Data mining technique i.e; Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables- also called the predictors.