Efficient water management in agriculture is crucial for improving productivity. In this study, Automated irrigation systems using soil moisture sensors for precise water discharge control and Internet of Things (IoT) technology were studied to achieve real-time data monitoring. The sensitivity of different types of soil moisture sensors varies, especially in field conditions. Hence, poses a challenge in optimizing irrigation water, leading to lowered productivity. Therefore, we provided insights into optimizing sensor selection and calibration for more effective water resource management in agriculture through performance evaluation of capacitive, resistive, and Time Domain Reflectometry (TDR) sensors in measuring soil moisture content under different soil types. The correlation between sensor sensitivity and the accuracy of soil moisture measurements under different soil types was studied. The laboratory experiment was conducted to evaluate th-e performance of factory-based calibrated soil moisture sensors. The performance of the soil moisture sensors was evaluated using Root Mean Squared Error (RMSE), Index of Agreement (IA), and Mean Bias Error (MBE). The result shows that the performance of the factory-based calibrated capacitive, resistive, and Time Domain Reflectometry (TDR) did not meet all the statistical criteria except the capacitive sensor for sand loamy. There was a strong positive relationship among sensors. The correlation between TDR and resistive moisture readings was 0.96, between TDR and capacitive moisture readings was 0.98, and between resistive and capacitive moisture readings was 0.97. The correction equations were developed using the laboratory experiment and validated in the field. The correction equations for capacitive, resistive, and TDR improved the accuracy in field conditions.
Makange et al. (Mon,) studied this question.