Multimodal Fusion for Automated Life Support and Space Farming
Year: 2026
Type: book-chapter
Abstract: Sustaining human life on long-duration missions require the automation of extraterrestrial greenhouses to ensure food security and oxygen production. These biological systems are highly sensitive, demanding the fusion of heterogeneous data sources—from real-time IoT soil sensors to multispectral satellite imagery—to maintain optimal conditions. We extend the Intelligent Farming (ICARA 2026) framework to address the unique complexities of space-based agriculture. The proposed solution utilizes a Two-Layer Analytics Architecture. Layer 1 (Edge Processing) is deployed on constrained devices like the NVIDIA Jetson, ingesting high-velocity IoT logs (Soil N, P, K, pH, and TDS) to perform immediate anomaly detection. Layer 2 (Cloud/Batch Processing) fuses this real-time stream with historical datasets and multispectral inputs to classify long-term crop suitability. Instead of simple averaging, we employ a probabilistic fusion of events derived from an ensemble of Random Forest (RF), XGBoost, and Support Vector Machines (SVM). This “Derived Event Fusion” allows the system to generate high-level alerts, such as combining a “Low Nitrogen” sensor event with a “Radiation Spike” forecast, significantly improving context awareness compared to raw data analysis. Experimental validation shows a 6% improvement in Weighted F1-Score accuracy over single-model baselines. Furthermore, the system demonstrates linear scalability, successfully processing up to 10,000 events per second. By optimizing for both low latency and high accuracy, this framework provides the robust decision-support required for closed-loop life support systems, ensuring crop sustainability in the harsh conditions of microgravity.
Source: Özgür Yayınları eBooks
Parent work: Statistical Methods and Data Analysis
Institutions: Bahçeşehir University, Balıkesir University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7208720173