Raman-based rapid phenotyping platform for smart space farming stress monitoring

Overview

Space farming operations expose crops to combinations of stressors such as elevated temperatures and high CO2 that trigger specific metabolic responses in plants. This solution adapts Raman spectroscopy, a rapid and non-destructive analytical technique, into a handheld phenotyping platform for monitoring live plants in near real-time. By coupling compact Raman hardware with automated chemometric and machine learning models, the platform aims to predict stress phenotypes directly within planetary habitats, enabling timely preventive interventions to safeguard crop health and yield.

Technical specifications

• Handheld Raman device for quick, non-destructive measurements on live leaves with minimal or no sample preparation, supporting compact and low-cost payloads.
• Chemometric analyses that deconvolute Raman spectra and identify metabolic signatures associated with specific stressors.
• Automated machine learning models built on the publicly available Rametrix LITE and PRO MATLAB toolboxes to classify spectra into user-defined stress and non-stress groups.
• Multi-stress validation framework combining Raman spectra with GC-MS metabolite profiling and photosynthetic measurements to confirm model predictions.
• Test environments including controlled growth chambers at Virginia Tech and the SAM habitat at Biosphere 2 for validation under individual and combined heat and elevated CO2 conditions.
• Model organism Arabidopsis thaliana, with extensibility to other crops and mission-relevant stress profiles.

Technology readiness level

The underlying Raman and chemometric methodology has been validated across multiple biological systems, including algae, cyanobacteria, bacteria, liver tissue, and human urine, where it successfully classified unknown spectra into defined phenotypic groups. The current project represents the first dedicated adaptation of this approach to plant stress phenotyping under space-relevant combined stressors. Planned validation includes generating Raman spectra from live Arabidopsis leaves under non-stress, single-stress, and combined heat and elevated CO2 conditions, followed by training predictive models and cross-validating them against GC-MS and photosynthetic data. Preliminary results from this work will support a future ROSES Space Biology NASA proposal to extend the platform to additional mission-related stresses.