Smart Root Health Monitoring System Using Low-Frequency Soil EM Signals

Authors

B. Sudhakiran

Assistant Professor, Department of ECE, Adikavi Nannaya University College of Engineering (India)

B. Varshitha

Student, Department of ECE, Adikavi Nannaya University College of Engineering (India)

B. Jhansi Kala

Student, Department of ECE, Adikavi Nannaya University College of Engineering (India)

Article Information

DOI: 10.51584/IJRIAS.2026.11060049

Subject Category: Machine Learning

Volume/Issue: 11/6 | Page No: 511-516

Publication Timeline

Submitted: 2026-06-04

Accepted: 2026-06-10

Published: 2026-06-22

Abstract

Root health plays a vital role in overall plant growth, crop yield, and sustainable agriculture. Early detection of root stress, diseases, and soil-related abnormalities is essential to prevent yield loss and ensure efficient resource utilization. Traditional root monitoring methods are invasive, labor-intensive, and often fail to provide real-time insights into underground conditions. This project proposes a Smart Root Health Monitoring System using low-frequency soil electromagnetic (EM) signals to non-invasively assess root zone conditions. The system employs embedded sensors and signal generation modules to transmit low-frequency EM waves through the soil. Variations in signal response are analyzed to detect changes in soil moisture, root density, root damage, and possible disease presence. Signal preprocessing, noise filtering, feature extraction, and machine learning techniques are applied to interpret soil-root interaction patterns and classify root health status. The proposed model enables continuous, real-time monitoring with minimal soil disturbance. It improves detection accuracy, reduces manual inspection effort, and provides a cost-effective solution for precision agriculture. This system can be integrated with IoT platforms for remote monitoring, data visualization, and smart irrigation management.

Keywords

Smart Agriculture, Root Health Monitoring, Low-Frequency Electromagnetic (EM) Signals

Downloads

References

1. Zheng, Z.; Xia, H.; Ambinakudige, S.; Qin, Y.; Li, Y.; Xie, Z.; Zhang, L.; Gu, H. Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China. Sustainability 2019, 11, 1160 .https://www.mdpi.com/1801160 [Google Scholar] [Crossref]

2. Sharma, S.K.; Kumar, A.; Gupta, R. Smart Soil Monitoring Using Machine Learning Techniques. 2023 IEEE International Conference on Advanced Computing and Communication Systems (ICACCS) 2023, 1–6. [Google Scholar] [Crossref]

3. doi:10.1109/ICACCS57279.2023.10265651.. https://ieeexplore.ieee.org/document/10265651?source=document-share&dld=Z21haWwuY29t [Google Scholar] [Crossref]

4. de Oliveira, L.R.; Kozusny-Andreani, D.I.; Monteiro, G.G.T.N.; Mendes, I.C.; Rossetto, R.; Vanzela, L.S.; Vazquez, G.H.; Navarrete, A.A. Denitrifying microbial genes quantification attests inference for potential N₂O emissions in sugarcane soils by enzymatic bioanalysis. Frontiers in Soil Science 2024, 4, 1536797. https://www.frontiersin.org/journals/soil- science/articles/10.3389/fsoil.2024.1536797/full [Google Scholar] [Crossref]

5. Liu, H.-Y. Irresponsibilities, inequalities and injustice for autonomous vehicles. Ethics and Information Technology 2017, 19, 193–207. https://rdcu.be/e9Ww9 [Google Scholar] [Crossref]

6. Zhou, Z.; Duan, S.; Nie, G.; Sun, Z.; Duan, L. Repurposing e-waste cathodes as catalysts for CO₂ reduction via the reverse water–gas shift reaction. Journal of Materials Chemistry A 2025, 13, 3402–3412. https://rdcu.be/e9Wzs [Google Scholar] [Crossref]

7. Huo, L.; Wu, Z.; Wu, J.; Gao, S.; Chen, Y.; Song, Y.; Wang, S. High-Precision Log-Ratio Spot Position Detection Algorithm with a Quadrant Detector under Different SNR Environments. Sensors 2022, 22, 3092.https://www.mdpi.com/3094942 [Google Scholar] [Crossref]

8. M.; Fattahi, J.; Ghnimi, S.; Ghayoula, R.; Boulejfen, N. Measuring Electromagnetic Properties of Vegetal Soil for Wireless Underground Sensor Networks in Precision Agriculture. Applied Sciences 2024, 14, 11884. https://www.mdpi.com/3094942 [Google Scholar] [Crossref]

9. Haque, E.U.; Shah, A.; Iqbal, J.; Ullah, S.S.; Alroobaea, R.; Hussain, S. Author Correction: A scalable blockchain based framework for efficient IoT data management using lightweight consensus. Scientific Reports 2024, 14, 9325. https://rdcu.be/e9WLK [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles