Prediction of Sugarcane Yields from Field Records using Regression Modeling

Abstract

Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The Paper Focuses On The Generation Of Multiple Regression Models For The Dataset Of Sugarcane Crop For Season Adasali, Suru And Preseasonal Method. The Intercept And Slope For Variables Are Calculated And Equation For Each Model Is Generated. Sample Of N,P,K And Other Are Considered For A Period Of 7 Years From 2012 To 2018. Data Of Experimentation Is Collected For Arid Region I.E. Pandharpur, Maharashtra State.