Low-Cost Irrigation Management System: Improving Data Confidence Through Artificial Intelligence

Abstract

A common scenario in the developing countries is the low income and less education of producers. Thus, the tools used for irrigation management must be cheap and easy to handle. In this work, an autonomous and low-cost network of micro-weather stations has been developed for irrigation management. Simulations were performed to evaluate the ability of intelligent systems to compute evapotranspiration with noisy and insufficient data. The network of micro-weather stations was then applied to autonomous irrigation management of a crop of bell peppers. Statistical analysis was performed on data from the developed system and a standard weather station. The results show no statistical difference between the values of evapotranspiration calculated with data from these two sources. The developed system performed with a coefficient of determination of 0.968, mean absolute error of 0.055 mm day −1 , and root mean square error of 0.063 mm day −1 . The study shows that low-cost intelligent systems can be used as viable tools for efficient irrigation management.

A Stochastic Bayesian Artificial Intelligence Framework to Assess Climatological Water Balance under Missing Variables for Evapotranspiration Estimates

“…Despite its high correlation with the observed value for the evapotranspiration metric, the primary limitation of this equation is its dependence on several climatological factors. Weather stations that can automatically provide the required data for the Penman-Monteith equation are unattainable to most rural producers, resulting in this method being unfeasible to estimate [40].…”