Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained...

Effective sensor denoising is crucial for accurate, real-time agricultural decision-support systems. This study explores the application of Unscented Kalman Filter (UKF) extensions on resource-constrained devices to improve sensor denoising and enhance the reliability of Internet of Things (IoT) based agricultural soil monitoring. The study was conducted in Ruhango district, Rwanda, utilizing a wireless sensor node equipped with a Raspberry Pi 5 (ARM v8) and an integrated seven-in-one soil sensor measuring temperature, humidity, electrical conductivity, pH, nitrogen, phosphorus, and potassium. The sensor was placed at a depth of 20 cm in ten cassava farms, collecting data every 30 min for eight months. Four real-time sensor denoising models were implemented: UKF, Cubature Kalman Filter (CKF), UKF with Artificial Neural Network (UKF_ANN), and UKF with Fuzzy Logic (UKF_FL). Models' performance was evaluated using boxplot, square root(R²), mean absolute error (MAE), root mean square error (RMSE), computation memory (CM), and computation time (CT). Data analysis was performed using Python 3.12 on ARM v8. Results demonstrated that CKF outperformed the other models, reducing RMSE by up to 32% and lowering CM and CT by 75%. CKF and UKF_ANN maintained the integrity of the censored data while effectively removing Gaussian, uniform, and salt-and-pepper noise, making them suitable for IoT-based soil monitoring systems.