Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device
Abstract 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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05427-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849334981268078592 |
|---|---|
| author | Egas Jose Armando Damien Hanyurwimfura Omar Gatera Kwang Soo Kim Athanase Nduwumuremyi |
| author_facet | Egas Jose Armando Damien Hanyurwimfura Omar Gatera Kwang Soo Kim Athanase Nduwumuremyi |
| author_sort | Egas Jose Armando |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1068ff0eefb04fdb9071378146105e73 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1068ff0eefb04fdb9071378146105e732025-08-20T03:45:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-05427-wEvaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained deviceEgas Jose Armando0Damien Hanyurwimfura1Omar Gatera2Kwang Soo Kim3Athanase Nduwumuremyi4African Center of Excellence in Internet of Things, University of RwandaAfrican Center of Excellence in Internet of Things, University of RwandaAfrican Center of Excellence in Internet of Things, University of RwandaDepartment of Agriculture Forestry and Bioresources, Seoul National UniversityRwanda Agriculture BoardAbstract 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.https://doi.org/10.1038/s41598-025-05427-wAgricultural soil sensor denoisingUnscented Kalman filtersCubature Kalman filterArtificial Neural NetworkFuzzy Logic |
| spellingShingle | Egas Jose Armando Damien Hanyurwimfura Omar Gatera Kwang Soo Kim Athanase Nduwumuremyi Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device Scientific Reports Agricultural soil sensor denoising Unscented Kalman filters Cubature Kalman filter Artificial Neural Network Fuzzy Logic |
| title | Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device |
| title_full | Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device |
| title_fullStr | Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device |
| title_full_unstemmed | Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device |
| title_short | Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device |
| title_sort | evaluation of advanced kalman filter on real time agricultural soil parameters through an iot resources constrained device |
| topic | Agricultural soil sensor denoising Unscented Kalman filters Cubature Kalman filter Artificial Neural Network Fuzzy Logic |
| url | https://doi.org/10.1038/s41598-025-05427-w |
| work_keys_str_mv | AT egasjosearmando evaluationofadvancedkalmanfilteronrealtimeagriculturalsoilparametersthroughaniotresourcesconstraineddevice AT damienhanyurwimfura evaluationofadvancedkalmanfilteronrealtimeagriculturalsoilparametersthroughaniotresourcesconstraineddevice AT omargatera evaluationofadvancedkalmanfilteronrealtimeagriculturalsoilparametersthroughaniotresourcesconstraineddevice AT kwangsookim evaluationofadvancedkalmanfilteronrealtimeagriculturalsoilparametersthroughaniotresourcesconstraineddevice AT athanasenduwumuremyi evaluationofadvancedkalmanfilteronrealtimeagriculturalsoilparametersthroughaniotresourcesconstraineddevice |