Enhancing agricultural internet of things data accuracy: Evaluating kalman filter-based sensor denoising techniques

Publication Type

Journal Article

Publication Date (Issue Year)

2025

Journal Name

Journal of Biosystems Engineering

Abstract

Purpose

With the rapid adoption of the Internet of Things (IoT) in agriculture, monitoring ecological parameters has become increasingly important for farmers aiming to optimize crop yields. However, sensor noise often hampers the collection of accurate data. This pioneering paper addresses the gap by evaluating real-time sensor denoising techniques using Kalman filter models on a Raspberry Pi 4.

Methods

A sensor node was deployed in the Ruhango District, Rwanda, for 6 months to address this. We implemented three models: the Unscented Kalman Filter (UKF), the Unscented Kalman Filter combined with Fuzzy Logic (UKF_FL), and the Cubature Kalman Filter (CKF), to process real-time data. The environmental parameters monitored included non-methanic hydrocarbon concentration (NMHC), nitrogen oxide concentration (NOx), nitrogen dioxide concentration (NO2), and air temperature (T). The performance of these models was assessed using the coefficient of determination, root mean square error, mean absolute error, and computation time and memory usage.

Results

Our findings indicate that the Unscented Kalman Filter and Fuzzy Logic (UKF_FL) outperformed the other models, achieving approximately 99% accuracy and a 25% reduction in computational memory usage compared to UKF.

Conclusion

This suggests that UKF_FL and CKF are highly suitable for providing an effective solution to the problem of sensor noise in real-time agricultural IoT applications.

Keywords

Real-time state prediction, Raspberry Pi 4, Sensor denoising

Rsif Scholar Name

Armando Egas Jose

Rsif Scholar Nationality

Mozambique

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Rwanda (UR), Rwanda

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