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Beginning in the 1950s and 1960s, an abundance of effort and initiative was focused on propelling the space industry outward for planetary exploration and habitation. During these early years, the push to take space science to new levels indirectly contributed to the evolution of another science field that would not fully surface until the early 1980s, environmental remediation. This field is associated with the remediation or cleanup of environmental resources such as groundwater, soil, and sediment. Because the space-exploration initiative began prior to the establishment of the U.S. Environmental Protection Agency (EPA) in December of 1970, many NASA Centers as well as space-related support contractors allowed for the release of spent chemicals into the environment. Subsequently, these land owners have been directed by the EPA to responsibly initiate cleanup of their impacted sites.

Metric analysis of research activity and technology development has become one of the deciding factors in whether or not the research of potential technologies receives the needed funding or a technology is incorporated into a system. It is difficult to accurately predict the configuration of an ALS system that will transport humans to the surface of Mars and support surface exploration. Determining which ALS research activities will support this effort is a very discretionary process, and there simply is not enough information to accurately make these types of decisions. Requirements change as research develops, and it is very difficult to create a metric that can accurately assess a potential or ongoing research project. The SSM team of the NJ-NSCORT has developed an internet platform to perform the assessment of potential technologies for the purpose of the development of an ALS system. The platform is called IFA and it has completed validation with current NJ-NSCORT projects.

The primary objective of the investigation was to evaluate the effects of induced perturbations in air temperature on the development of the tomato plant, while correlating a plant feature for use with machine vision non-contact sensing technologies, and allow for eventual integration into a non-invasive plant-based environmental control system. Real-time information of plant growth responses to steady-state and changing air temperature regimes were measured (i.e. dry weight). There was a positive correlation of the profile machine vision images with dry weight. Therefore, machine vision could be used for plant developmental predictions and development of a control system for maintaining plant schedules.