Rapid, nondestructive quantification of calcium and vitamin C in broccoli microgreens

Overview

This research develops hyperspectral imaging models that can rapidly and nondestructively quantify calcium and vitamin C content in broccoli microgreens. By correlating short-wave infrared (SWIR) image reflectance with ground-truth nutrient values from high-performance liquid chromatography (HPLC), the project aims to enable autonomous, real-time nutrient assessment of crops grown under varying light conditions. Beyond measurement, the models will identify optimal lighting strategies for upregulating key nutrients, supporting both deep space crewed missions and controlled environment agriculture on Earth.

Technical specifications

• Imaging system: Top-down canopy imaging of microgreens using a short-wave infrared camera with incandescent illumination and optical-grade Spectralon for radiometric calibration.
• Ground-truth analysis: High-performance liquid chromatography (HPLC) to determine precise calcium and vitamin C (ascorbic and dehydroascorbic acid) concentrations.
• Prediction modeling: Partial least squares regression and other AI/machine learning algorithms trained to correlate hyperspectral reflectance data with HPLC-derived nutrient values.
• Experimental design: Controlled block design with three light treatments (red/blue, white, and red/blue + UV) to evaluate nutrient variation across lighting recipes.
• Species focus: Broccoli microgreens, with species- and cultivar-specific model calibration.

Technology readiness level

The approach builds on established hyperspectral imaging and chemometric modeling techniques previously validated for calcium and vitamin C in other plant species. Current work focuses on adapting these methods to microgreens and validating nutrient-light relationships through controlled grow experiments. The imaging system is in early development, with future validation planned to train, test, and refine prediction models before deployment in operational grow environments.