Effects of artificial lighting on the detection of plant stress with spectral reflectance remote sensing in bioregenerative life support systems

Abstract

Plant-based life support systems that utilize bioregenerative technologies have been proposed for long-term human missions to both the Moon and Mars. Bioregenerative life support systems will utilize higher plants to regenerate oxygen, water, and edible biomass for crews, and are likely to significantly lower the ‘equivalent system mass’ of crewed vehicles. As part of an ongoing effort to begin the development of an automatic remote sensing system to monitor plant health in bioregenerative life support modules, we tested the efficacy of seven artificial illumination sources on the remote detection of plant stresses. A cohort of pepper plants (Capsicum annuum L.) were grown 42 days at 25 °C, 70% relative humidity, and 300 μmol m−2 s−1 of photosynthetically active radiation (PAR; from 400 to 700 nm). Plants were grown under nutritional stresses induced by irrigating subsets of the plants with 100, 50, 25, or 10% of a standard nutrient solution. Reflectance spectra of the healthy and stressed plants were collected under seven artificial lamps including two tungsten halogen lamps, plus high pressure sodium, metal halide, fluorescent, microwave, and red/blue light emitting diode (LED) sources. Results indicated that several common algorithms used to estimate biomass and leaf chlorophyll content were effective in predicting plant stress under all seven illumination sources. However, the two types of tungsten halogen lamps and the microwave illumination source yielded linear models with the highest residuals and thus the highest predictive capabilities of all lamps tested. The illumination sources with the least predictive capabilities were the red/blue LEDs and fluorescent lamps. Although the red/blue LEDs yielded the lowest residuals for linear models derived from the remote sensing data, the LED arrays used in these experiments were optimized for plant productivity and not the collection of remote sensing data. Thus, we propose that if adjusted to optimize the collection of remote sensing information from plants, LEDs remain the best candidates for illumination sources for monitoring plant stresses in bioregenerative life support systems.

Canopy structure and imaging geometry may create unique problems during spectral reflectance measurements of crop canopies in bioregenerative advanced life support systems

“…8). Although results were very consistent with published reports on spectral reflectance of greenhouse grown plants (Schuerger et al 2003), crops grown in simulated ALS systems (Omasa 2001 ;Schuerger & Richards 2006) and traditional agricultural ecosystems (Lillesand & Kiefer 1994;Lichtenthaler 1996 ;Schowengerdt 1997), the results indicated that unique remote sensing models are likely to be required for individual plant species because the ranges of normal chlorophyll levels in healthy leaves differed significantly among the three plant species tested, and, thus, one model would not be precise enough to yield clear information on a diversity of crops. For example, (i) the healthy lettuce canopies fell within the range of nutritionally stressed tomato and pepper leaves and (ii) slope values for the models between healthy and stressed plants were all different among tomato, pepper and lettuce plants.…”

“…The different lighting systems pose unique challenges to collecting useful remote sensing data, and few studies now exist that have characterized the effects of these lighting systems on remote sensing measurements. In a recent paper, Schuerger & Richards (2006) examined seven different artificial lighting systems for remote sensing measurements and found that although several types of lamp could be used for collecting useful spectral reflectance measurements, tungsten-halogen and LED lamps offer the most advantages over other types. Third, the background surfaces of growing trays in bioregenerative ALS systems will likely be highly reflective white or mirrored surfaces (Figs 1(d) and (e)) in order to redirect as much useful photosynthetic active radiation (PAR ; 400-700 nm) back up and into the plant canopies.…”

A Complete MCDM Model for NPD Performance Assessment in an LED-Based Lighting Plant Factory

“…LEDs can control the spectral output of a lighting system for specific crops and even be modified over the course in view of a photoperiod or growth cycle. Special lighting modes can also be used to solve plant disease or injury problems [14]. Additionally, LED lighting systems can be configured to produce very high light levels, have a very long operating life, and can be turned on and turned off instantly without warm-up time.…”

“…At the outset of constructing the experts' questionnaires, the elements of objectives such as management team, financial funding, product and technology, and marketing are used to obtain the relevance and level of impact through directrelation analyses, as shown in Table 6. The sum of on each column and the sum of on each row, respectively, are then calculated, as shown in (14), where denotes the level of impact directly and affects other criteria; denotes the level; and criterion is directly affected by other criteria. + denotes relevance, signifying the intensity of the relationship between criteria.…”

“…In particular, if − is a positive value, it suggests that affects criterion ; conversely, if − is a negative value, it suggests that affects criterion . In order to obtain the relevance and level of impact among objectives, the matrix values are substituted into (14) to yield and , which, in addition to a summary, are found in Table 16. In particular, + represents the relevance value between objectives, whereas − denotes the level of impact or impact value.…”