Prediction of soil moisture using machine learning techniques: A case study of an IoT-based irrigation system in a naturally ventilated polyhouse

Abstract

The agricultural sector faces a massive challenge in enhancing food production for the growing population with limited water resources. For effective and optimum utilization of fresh water, developing smart irrigation systems based on the internet of things (IoT) is essential for scheduling irrigation based on crop water requirements. In this study, an IoT-based irrigation system was developed and evaluated inside a greenhouse located in the experimental fields of Indian Council of Agricultural Research-Central Institute of Agricultural Engineering (ICAR-CIAE), Bhopal, India. Data on microenvironmental parameters such as temperature, relative humidity, light intensity, soil temperature and soil moisture were collected from the sensors developed inside the greenhouse. Soil moisture was predicted based on the field data collected via different machine learning techniques, such as the decision tree (DT), random forest (RF), multiple linear regression (MLR), extreme gradient boosting (XGB), K-nearest neighbour (KNN) and artificial neural network (ANN) methods, with three input combinations. The ANN (coefficient of determination [R²] = 0.942, 0.939) models performed well but were found to be less effective than the RF (R² = 0.991, 0.951) and XGB (R² = 0.997, 0.941) models in the training and testing phases, respectively. The RF and XGB models outperformed the other models, while the MLR (R² = 0.955, 0.875) technique underperformed. With respect to both the testing and training datasets, the models trained with all four inputs outperformed the models trained with two or three inputs.