Estimation, forecasting, and prediction of arecanut yield considering the effect of external parameters: A systematic review

Abstract

Intensive agriculture necessitates precise yield forecasting or yield prediction methods, which is technically challenging due to the dependence of yield productivity on the climatic, ecological and agronomic aspects and their effects. Various big data analytics framework has been used to forecast the arecanut crop yield considering hydrological, soil and meteorological datasets. This survey examines the existing literature on arecanut yield forecasting and also explores the widely used prominent methodologies and the effect of different attributes on the same. The study emphasises the problems and recent research developments in the Areca cropping system concerning the effect of weather, soil and crop genotype external parameters. By paving the route for future agricultural research, it also strengthens the substantial impact of these parameters on Arecanut diseases.