Ameliorated Methodology for the Design of Sugarcane Yield Prediction Using Decision Tree

Abstract

The productivity and quality of a Crop depends upon several parameters including but not limited to environment parameters like temperature, humidity, rainfall, wind, the quality and quantity of the pesticide, type, quantity and quality of fertilizer and so on. High productivity and yield is of utmost essentiality in a country like India where the growing population demands more grains that the field produces right now. Thus there is an urgent need to bring in more scientific studies in this direction. As amount of agricultural fields is limited and cannot be increased, it is important to get maximum productivity out of each crop. Past records of yield of a crop in different months, the known crop diseases and the weather plays a good indication as to what should be the ideal condition for high yield of a crop. However the dependencies among the parameters are so fuzzy that it is difficult to estimate the exact yield and need for exact quantity of pesticides and fertilizer to minimize the risk of crop disease, crop failure and to improve the yield. In this work a Framework is proposed that can be used to provide guidance to the farmer to detect the fault at the earlier stage to reduce losses. Decision tree, an efficient, globally competitive and vibrant classifier is used to predict sugarcane yield.