Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms

Abstract

The coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradient boosting (XGB) and random forest-extreme gradient boosting (XGB-RF) were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest and lowest temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout based on four scenarios (input combinations). The main findings were; the highest CU value was 86.7% in the square system of 2520 sprinkler under 200 kPa, 0.5 m height and 0.855 m3/h (Nozzle 2.5 mm). Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge and 1 m height. For applied machine learning, the highest values of R2 were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R2 were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the sprinkler height had the lowest impact on modeling of the water distribution uniformity.

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

“…Several examples of applications of machine learning in precision agriculture [51] are reported, i.e., soil properties detection [52][53][54], crop yield predictions [55][56][57][58][59], disease [60][61][62][63] and weed detection [64][65][66], site-specific irrigation [67][68][69], and livestock production and management [70][71][72]. One of the most in-depth topics is the analysis of plant health with hyperspectral data [73].…”