Smart reference evapotranspiration using Internet of Things and hybrid ensemble machine learning approach

Abstract

Reference Evapotranspiration (ETo) is the cornerstone of efficient water utilization for sustainability in agriculture. The standard Penman–Montieth (PM) approach of Reference Evapotranspiration (ETo), is complex due to the involvement of an extensive set of climatic conditions. The existing solutions of simplification of ETo predictions are not in accordance with the Penman–Montieth approach. A hybrid ensemble machine learning approach for simplification of ETo prediction is proposed using the Internet of Things(IoT) based crop field sensed climatic data. The proposed hybrid ensemble model is implemented with an Artificial Neural Network (ANN) and regression models. The proposed solution is unique for its utilization of flexible climatic conditions and in accordance with the standard Penman–Montieth (PM) approach. The proposed solution is able to predict daily ETo from only temperature and also can adjust ETo according to wind speed, humidity, and sunshine duration. The assessment of the proposed model exhibits a high coefficient of determination (R2) of 0.94 compared to 0.91 from the basic ANN model. The proposed hybrid ensemble model also exhibits a low RMSE of 0.86, MAE of 0.75 mm day−1, and MAPE of 15.05%, compared to 0.91, 0.75 mm day−1, and 20.40% from the basic ANN model. The ETo predictions by the proposed hybrid ensemble model also exhibit a higher Pearson correlation coefficient of 0.917 with the ETo by the Penman–Montieth (PM) approach, compared to 0.778 by the basic ANN model. The statistics reveal the accuracy and goodness of fit of the proposed hybrid ensemble machine learning model.