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

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.