Machine Learning for Space Gardening

Abstract

Sustained human presence in space requires the development of new technologies to maintain environment control, provide water, oxygen, food and to keep astronauts healthy and psychologically fit. The EDEN NEXT GEN project works along the roadmap of building a flight-ready bio-regenerative life support system within this decade. Being part of that project, we are concerned with detecting unhealthy system states and plant stress in the context of extraterrestrial horticulture. In this talk, I will introduce different classical and deep-learning-based methods for finding anomalies in time series and present our latest results on differences regarding the types of anomalies these methods can find.