Machine Learning Approaches to Estimate Flow Rate of Drip Irrigation Emitter Based on Structure Parameters and Pressure

Abstract

The agricultural water-saving irrigation systems need to be adapted to local conditions, and irrigation emitters with different flow rates need to be designed to meet actual needs. In this study, machine learning approaches were used to predict the flow rate of the labyrinth emitters. The structural parameters of the emitter [number of channel units (N), channel unit length (L), channel width (W), tooth height (H) and channel depth (D)] and working pressure (P) were considered as independent variables. The applied machine learning models included k-nearest neighbor (KNN), multi-layer perceptron (MLP), support vector machine (SVM) and radial basis function artificial neural network (RBF-ANN). The accuracy of the machine learning model was evaluated by the mean square error (MSE) and coefficient of determination (R2). The results show that the MSE and R2 of the RBF-ANN model were higher than the corresponding parameters of the other three models, indicating the RBF-ANN model can predict the flow rate of the labyrinth emitter more accurately. The research provides a reference for the rapid design of emitters with different flow rate. It is helpful to realize the automation of agricultural irrigation, and then realize the construction of a smart agricultural irrigation system.