Regression Analysis for Evaluation of Evopotranspiration for Agriculture Water Requirement

Abstract

India Grapes Farming is very important sector of agriculture. Due to volatile weather condions and  scare water resources irrigation management becomes challenging aspect for grapes farmers. The proposed  system considers all challenges of Grapes  Farming   and developed automated irrigation system. The water resource management is the challanging task in agriculture. To Achieve water management in agriculture field many new technology has been proposed in recent research work, however  very few work focused on productivity of crop while optimizing water resources. Proposed model  considers all aspect  of Irrigation like  weather, crop growth stages and canopy coverage.  Morever system also considers landscape information like slope of the farm area, soil type, elevation level etc.

      Proposed system predicting most enflucing parameter for evaluation of Evopotranspiration(ET0) using  Machine Learning Regression analysis Algorithm. ET0 evaluation requires many  parametrs like day Light hours, Vappour pressure, Longwave Radiation, Soil Heat Flux etc those are not readily available or one can not record it direcly. These parametrs are claculated using web scraped data then used in  ET0 evaluation process in addition  to crop growth data and landscape information. This predictive model  provides optimised parameter for evaluation of  ET0. Proposed model showing very less error deviation in  Actaul and precited values of ET0.