Dynamic Water Stress Threshold Determination for Precision Deficit Irrigation Control using Progressive Clustering Approach

Abstract

Precision deficit irrigation offers a solution to the increasing global pressure on freshwater resources occasioned by a rising demand for agricultural outputs to support a growing human population. Plant physiological responses to water deficit are describe in terms defining severity of water stress. Implementation of deficit irrigation control strategies capable of achieving the twin goals of maximizing potential yield and minimizing cumulative water consumption requires the identification of water deficit levels corresponding to significant stress thresholds to ensure memory initiation and prevent permanent damage to the crop. In this contribution machine learning approaches are implemented for dynamic identification of water stress thresholds during deficit irrigation of potted maize plants. K-means clustering is initally applied to delineate three zones of water stress described as ”no stress”, ”mild stress” and ”high stress” for chronologically segmented data points. Least squares-based polynomial curve fitting is employed to mathematically represent the dynamic progression of stress cluster centroids, with accuracy values ranging between 90% and 98%.