Electrical sensor platform with machine learning for early detection of plant water and nutrient deficiency
A sensor-based platform that uses electrical signal measurements and machine learning algorithms to real-time quantify plant water and nutrient deficiency. Validated on oat and canola crops, it identifies stress within 30 minutes with over 92% accuracy, enabling precision fertigation to improve productivity while minimizing nutrient inputs.
Overview
This solution offers an early detection platform for plant water and nutrient deficiency using electrical sensors combined with machine learning algorithms. By measuring electrical signal variations in plants, the platform can identify drought and nutrient stress within 30 minutes, enabling timely and precise fertigation decisions. The technology addresses the need for efficient nutrient and water management in crop production, helping growers optimize inputs while maintaining or improving plant productivity.
Technical specifications
Key features:
- Electrical signal measurement – Non-invasive sensors capture plant signal potentials that correlate with nutrient and water status, leveraging the fact that plants uptake charged nutrient ions from soil
- Machine learning analysis – Algorithms such as random forest process sensor datasets to identify stressful plant conditions with over 92% accuracy and less than 4% root mean square error
- Rapid detection – Provides actionable plant health gradients within a 30-minute measurement window
- Validated crop applications – Tested on oat and canola crops under greenhouse conditions with varying watering levels and nitrogen rates
- Future real-time capability – Planned upgrades include transitioning from offline to online mode with embedded or cloud-based AI algorithms, and integration with fertigation systems for automated nutrient and water management
Technology readiness level
The platform has been validated through greenhouse studies on oat and canola crops, demonstrating reliable detection of drought and nutrient deficiency with high accuracy. Currently operating in offline mode, the technology is at a stage where further development is planned to enable real-time monitoring and automated fertigation control. Next steps include hardware and software updates for online operation and field trials integrating the platform with fertigation systems to confirm its ability to optimize nutrient management in production settings.
About Agriculture and Agri-Food Canada
Agriculture and Agri‑Food Canada (AAFC) is a national science‑based department of the Government of Canada, headquartered in Ottawa, operating a coast‑to‑coast network of research centres and experimental farms—one of the country’s largest agri‑food R&D enterprises. Industry collaborators access co‑located laboratories, field sites, and controlled‑environment facilities embedded in major agricultural regions, enabling prototyping and validation across varied climates and production systems. Partnership mechanisms include collaborative R&D agreements, facility access, and on‑farm living‑lab models that connect researchers with producers, commodity groups, and technology providers for real‑world trials. Research is supported by federal appropriations and often leverages competitive national programs, including those from the Natural Sciences and Engineering Research Council and other Government of Canada initiatives. An internal intellectual property and commercialization function manages patents, licensing, and collaboration frameworks to translate discoveries into practical tools for industry.
Inquire about this partnering listing
Looking for
delivered to your inbox.