Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Abstract

Accurate estimation of Reference Evapotranspiration (ET0) is important for efficient management and conservation of irrigation water. Existing methods of ET0 rate determination are complex for application at the farmer level. Apart from standard methods of ET0 determination, many data-driven soft computing approaches were also proposed to determine the ET0 with limited data set. We proposed a temperature and humidity-based ML approach for ET0 rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET0) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET0-rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET0 rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET0 rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET0 is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.

Keywords

• Internet of Things (IoT)
• reference evapotranspiration (E₀)
• support vector machine (SVM)
• Gaussian Naive Bayes (GNB)
• K-nearest neighbour (KNN)
• artificial neural network (ANN)