Location: Environmental Microbial & Food Safety Laboratory
Project Number: 8042-42000-021-041-T
Project Type: Trust Fund Cooperative Agreement
Start Date: Oct 1, 2025
End Date: Jul 31, 2028
Objective:
The objective of this research is to develop image-fusion methods that enable real-time monitoring of safe and secure production of plants in space. Combined use of multiple imaging technologies will be explored to enhance early detection of microbial contamination and stress conditions through AI-based analysis.
Approach:
This research will develop and validate advanced image-fusion methods that combine data from multiple imaging modalities (e.g., hyperspectral, thermal, and fluorescence) to enable real-time monitoring of plant health and safety in space environments. Current imaging systems in controlled environments often function independently, limiting their ability to detect early signs of contamination or stress. The proposed approach will focus on algorithm development for fusing multimodal image data at pixel, feature, and decision levels, with AI-based models for microbial contamination and plant stress indicators. Training datasets will be generated from simulated space crop chambers under controlled stress and contamination scenarios. The methods will be optimized for computational efficiency to allow real-time processing on embedded systems, and results will be presented through a graphical user interface to support user decision-making in space-based plant production systems.