Human exploration missions to the Moon or Mars might be helped by the development of a bioregenerative advanced life-support (ALS) system that utilizes higher plants to regenerate water, oxygen and food. In order to make bioregenerative ALS systems competitive to physiochemical life-support systems, the ‘equivalent system mass’ (ESM) must be reduced by as much as possible. One method to reduce the ESM of a bioregenerative ALS system would be to deploy an automated remote sensing system within plant production modules to monitor crop productivity and disease outbreaks. The current study investigated the effects of canopy structure and imaging geometries on the efficiency of measuring the spectral reflectance of individual plants and crop canopies in a simulated ALS system. Results indicate that canopy structure, shading artefacts and imaging geometries are likely to create unique challenges in developing an automated remote sensing system for ALS modules. The cramped quarters within ALS plant growth units will create problems in collecting spectral reflectance measurements from the nadir position (i.e. directly above plant canopies) and, thus, crop canopies likely will be imaged from a diversity of orientations relative to the primary illumination source. In general, highly reflective white or polished surfaces will be used within an ALS plant growth module to maximize the stray light that is reflected onto plant canopies. Initial work suggested that these highly reflective surfaces might interfere with the collection of spectral reflectance measurements of plants, but the use of simple remote sensing algorithms such as 760/685 band ratios or normalized difference vegetation index (NDVI) images greatly reduced the effects of the reflective backgrounds. A direct comparison of 760/685 and NDVI images from canopies of lettuce, pepper and tomato plants indicated that unique models of individual plants are going to be required to properly assess the health conditions of canopies. A mixed model of all three plant species was not effective in predicting plant stress using either the 760/685 or NDVI remote sensing algorithms.
