IoT-Enabled Electrical Impedance Tomography Platform for Real-Time Root Phenotyping and Subsoil Stress Detection
A wireless IoT-based Electrical Impedance Tomography (EIT) sensing and imaging platform that non-invasively monitors root systems in real time. It enables detection of biotic stresses such as clubroot and nematode parasitism, supports identification of novel tolerance traits, and advances understanding of rhizosphere interactions for crop breeding and precision agriculture.
Overview
This solution is an IoT-enabled Electrical Impedance Tomography (EIT) phenotyping platform designed to image and monitor plant root systems in real time without disturbing the soil. By measuring and reconstructing geo-electrical signals from subsoil sensors, the system can distinguish healthy plants from those affected by soil-borne pathogens and pests. The technology has already been validated against clubroot caused by Plasmodiophora brassicae in oil-seed rape, and is now being extended toward nematode detection. The platform offers plant breeders, agronomists, and precision agriculture companies a new tool for identifying root traits linked to stress tolerance and for studying plant–soil interactions in the rhizosphere.
Technical specifications
- Non-invasive imaging: EIT sensors reconstruct 3D root architecture and soil conductivity changes without destructive sampling.
- IoT wireless architecture: Core geo-electrical sensors are being developed as wireless IoT nodes for field and greenhouse deployment.
- Edge and cloud computing: Designed with edge computing capability to ensure reliability, with data aggregation through a cloud-compatible hub.
- Robustness testing: Systems are validated under extreme environmental conditions to address connectivity and durability challenges.
- Statistical sensitivity: ANOVA-based validation showed statistically significant separation between healthy and infected plants (p < 0.001) from 23 days post-infection.
- Scalable experimental design: Future validation includes a minimum of five replicates per condition across healthy and nematode-parasitized plants.
Technology readiness level
The platform is at an early-to-mid stage of development. EIT-based detection of clubroot in oil-seed rape has been experimentally validated under controlled conditions over a 35-day growth period. The current focus is on miniaturizing and wireless-enabling the sensor system, building in edge computing, and extending validation to nematode parasitism and broader environmental conditions. Commercial deployment in field-scale agricultural settings will require further robustness testing and field trials.
About University of Manchester
The University of Manchester is a large, comprehensive public research university with a city‑centre campus anchored in a major innovation district. Purpose‑built graphene facilities provide industry access, prototyping and scale‑up support in collaboration with university engineers. The university’s integration with Manchester University NHS Foundation Trust through the Manchester Academic Health Science Centre enables translational studies and access to significant clinical settings. Its location within the Oxford Road Corridor links campus to Manchester Science Park and Citylabs, placing partners near established lab infrastructure and hundreds of innovation‑led firms. Research is supported by competitive funding from UKRI councils, NIHR and major research charities; a dedicated technology transfer office, the University of Manchester Innovation Factory, manages IP, licensing and spinouts.
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