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

Egas Jose Armando, Damien Hanyurwimfura, Omar Gatera, Kwang Soo Kim, Athanase Nduwumuremyi

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Egas Jose ArmandoAfrican Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda. egas.j.armando@uem.ac.mz.ORCID http://orcid.org/0000-0003-1983-3574

Damien HanyurwimfuraAfrican Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda.ORCID http://orcid.org/0000-0002-5001-377X

Omar GateraAfrican Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda.ORCID http://orcid.org/0000-0002-6290-1256

Kwang Soo KimDepartment of Agriculture Forestry and Bioresources, Seoul National University, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0003-2284-4389

Athanase NduwumuremyiRwanda Agriculture Board, Kigali, Rwanda.ORCID http://orcid.org/0000-0001-6275-2919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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(R

Indexed as

Agricultural soil sensor denoisingArtificial Neural NetworkCubature Kalman filterFuzzy LogicUnscented Kalman filters

Identifiers

PMID40596011

DOI10.1038/s41598-025-05427-w

PMCPMC12217877

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.