Self Calibration of Soil Moisture Sensors for Automatic Irrigation Systems

Abstract

Soil moisture monitoring is essential for efficient irrigation management in precision agriculture, yet sensor calibration remains a major bottleneck, often requiring labor-intensive manual adjustments. Inaccurate soil moisture readings due to sensor drift or manufacturing variability compromise irrigation efficiency and crop health. To address this, this thesis explores automated calibration strategies that combine machine learning and data assimilation techniques to enhance the reliability of soil moisture measurements. A first contribution is the development of a deep learning-based self-calibration algorithm that predicts the soil moisture sensor reading at field capacity using short sequences of uncalibrated sensor outputs. This model, built upon a hybrid CNN-DNN architecture, learns from synthetic data generated through HYDRUS-1D simulations across diverse soil textures and realistic hydrological conditions. By embedding soil-specific thresholds such as saturation and field capacity into the learning process, the approach eliminates the need for manual tuning and mitigates sensor drift, offering scalable calibration in real-world scenarios. In parallel, a data assimilation-based framework using the Iterative Ensemble Smoother and Particle Filter has been proposed. This method dynamically updates sensor parameters by integrating real-time field data with physically-based simulations. It enforces physical constraints on key variables, ensuring that updated parameters remain within soil-specific bounds. The approach demonstrates high adaptability to different environmental conditions and sensor error patterns, significantly reducing bias and improving irrigation performance. Both methods were experimentally validated under controlled and field conditions using an IoT-enabled infrastructure and high-precision reference instruments. The results confirmed the practical applicability, accuracy, and complementary strengths of the proposed approaches, establishing a solid foundation for scalable, autonomous soil moisture sensor calibration in smart irrigation systems.