International Journal of Astrobiology



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


Andrew C. Schuerger a1 and Jeffrey T. Richards a2
a1 Department of Plant Pathology, University of Florida, Building M6-1025, Space Life Sciences Lab, Kennedy Space Center, FL 32899, USA e-mail: acschuerger@ifas.ufl.edu
a2 Dynamac Corporation, Building M6-1025, Space Life Sciences Lab, Kennedy Space Center, FL 32899, USA

Article author query
schuerger ac   [PubMed][Google Scholar] 
richards jt   [PubMed][Google Scholar] 

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.

(Received February 2 2006)
(Accepted July 7 2006)
(Published Online September 19 2006)


Key Words: CELSS; ALS; bioregenerative life support system; plant stress; remote sensing.