Astro Cultivators: Autonomous Growth System for Space Farming based on Machine Vision and Multi-Sensor Fusion

Abstract

The autonomous space farming system could be used to grow self-pollinating crops in space exploration missions that require no human intervention. Our goal is to utilize hyper spectral imaging and traditional imaging to allow for machine learning models to be used and retrained for not only our crops, but others as well. Temperature, humidity, and pressure sensors coupled with a robotic arm will care and tend to the crops at every stage of development. This system can decrease crew members’ input needed to operate the plant growth systems in deep space by providing autonomous monitoring, real-time data reporting, ambient environment management, automatic harvesting, and cleaning. Crops with high nutrient content, high acceptability ratings, dwarf growth habits, and short harvest cycles (Sugar Ann Snap Peas and Red Robin Tomato) will initially be grown in this modular system which has the potential to be scale up or down, depending on the mission. A variety of “pick and eat” crops can be grown to provide palatable, safe, nutritious foods that are familiar foods for crews to consume. To reduce resupply from Earth, the seeds from these crops have the potential to be saved and used to grow continuous generations of crops.