Utilization of Data Mining Methods to Investigate Crop Yield Forecast

Abstract

Environmental vary and the shrink of accessible farming area are two of the mainly significant elements that influence worldwide food production particularly as far as wheat stores. A regularly expanding total population puts an immense interest on these assets. Thusly, there is a critical need to improve sustenance creation. This examination investigates another methodology in the manner analyses are done. This is done through the presentation of new strategies for examinations, for example, data mining and online analytical process in the approach. Also, this study endeavours to give a superior comprehension of the impacts of both steady variety factors, for example, soil type, precipitation and temperature on the crop yields. The study activities exposed that crop yield was mainly reliant upon precipitation and temperature. Also, it demonstrated that precipitation consistently influenced the temperature and soil type because of the moisture maintenance of yield developing areas. Outcome from the regression analyses, demonstrated that the statistical forecast of crop yields from past data, might be upgraded by data mining methods.