Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-17T19:11:34.875Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  22 May 2007

Andrew C. Schuerger ,
Kenneth L. Copenhaver ,
David Lewis ,
Russell Kincaid  and
George May
Andrew C. Schuerger
Affiliation:
Department of Plant Pathology, University of Florida, Building M6-1025, Space Life Sciences Lab, Kennedy Space Center, FL 32899, USA e-mail: [email protected]
Kenneth L. Copenhaver
Affiliation:
Institute for Technology Development, 2407 South Neil Street, Suite 2, Champaign, IL 61820, USA
David Lewis
Affiliation:
Institute for Technology Development, Building 1103, Suite 118, Stennis Space Center, MS 39529, USA
Russell Kincaid
Affiliation:
Institute for Technology Development, Building 1103, Suite 118, Stennis Space Center, MS 39529, USA
George May
Affiliation:
Institute for Technology Development, Building 1103, Suite 118, Stennis Space Center, MS 39529, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

ALSbioregenerative life support systemsCELSSremote sensingspectral reflectance
Type
Research Article
Information
International Journal of Astrobiology , Volume 6 , Issue 2 , April 2007 , pp. 109 - 121
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Brown, C.S., Schuerger, A.C. & Sager, J.C. (1995). Growth and photomorphogenesis of pepper plants under red light-emitting diodes with supplemental blue or far-red lighting. J. Amer. Soc. Hort. Sci. 120, 808–813.CrossRefGoogle ScholarPubMed
Drysdale, A.E., Ewert, M.K. & Hanford, A.J. (1999). Equivalent System Mass studies of missions and concepts. SAE Technical Paper Series 1999-01-2081. SAE International Publishing, Warrendale, PA.Google Scholar
Eckart, P. (1996). Spaceflight Life Support and Biospherics. Kluwer/Microcosm Press, Dordrecht.CrossRefGoogle Scholar
Ewert, M.K., Drysdale, A.E., Hanford, A.J. & Levri, J. (2001). Life Support Equivalent System mass predictions for the Mars dual lander reference mission. SAE Technical Paper Series 01-2358. SAE International Publishing, Warrendale, PA.CrossRefGoogle Scholar
Lichtenthaler, H.K. (1996). Vegetation Stress, p. 649. Gustav Fischer, New York.Google Scholar
Lillesand, T.M. & Kiefer, R.W. (1994). Remote Sensing and Image Interpretation, p. 750. Wiley, New York.Google Scholar
Mao, C. (2000). Focal plane scanner with reciprocating spatial window. US Patent 6,166,373.Google Scholar
Ming, D.W. & Henninger, D.L. (1989). Lunar Base Agriculture: Soils for Plant Growth, p. 255. American Society of Agronomy, Crop Science Society of America and Soil Science Society of America, Madison, WI.CrossRefGoogle Scholar
Norikane, J., Goto, E., Kurata, K. & Takakura, T. (2003). A new relative referencing method for crop monitoring using chlorophyll fluorescence. Adv. Space Res. 31, 245–248.CrossRefGoogle ScholarPubMed
Olson, R.L., Oleson, M.W. & Slavin, T.J. (1988). CELSS for advanced manned mission. HortScience 23, 275–293.CrossRefGoogle ScholarPubMed
Omasa, K. (2001). Phytobiological IT in CELSS. In Advanced Technology of Environment Control and Life Support, ed. Tako, Y., Shinohara, M., Komatsubara, O. & Nitta, K., pp. 244–251. Institute for Environmental Sciences, Tokyo.Google Scholar
Schowengerdt, R.A. (1997). Remote Sensing: Models and Methods for Image Processing, p. 522. Academic Press, New York.Google Scholar
Schuerger, A.C. & Brown, C.S. (1997). Spectral quality affects disease development of three pathogens on hydroponically grown plants. HortScience 32, 96–100.Google Scholar
Schuerger, A.C., Capelle, G.A., De Benedetto, J.A., Mao, C., Thai, C.N., Evans, M.D., Richards, J., Blank, T. & Stryjewski, E.C. (2003). Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.). Remote Sens. Environ. 84, 572–588.CrossRefGoogle Scholar
Schuerger, A.C. & Mitchell, D.J. (1992). Effects of temperature, hydrogen ion concentration, humidity, and light quality on disease severity of Fusarium solani f. sp. phaseoli. Can. J. Bot. 70, 1798–1808.Google Scholar
Schuerger, A.C. & Richards, J.T. (2006). Effects of artificial lighting on the detection of plant stress with spectral reflectance remote sensing in bioregenerative life support systems. Int. J. Astrobiol. 5, 151–169.Google Scholar
Tibbits, T.W. & Alford, D.K. (1982). Controlled ecological life support system: use of higher plants. NASA Conference Publication 2231, p. 81. NASA Ames Research Center, Moffett Field, CA.Google Scholar
Wheeler, R.M. & Martin-Brennan, C. (2000). Mars greenhouses: concepts and challenges, p. 140. NASA Technical Memorandum 2000-208577, Kennedy Space Center, FL.Google Scholar
Wheeler, R.M. et al. (2003). Crop production for advanced life support systems: observations from the Kennedy Space Center Breadboard Project. NASA Technical Memorandum 2003-211184, p. 58. Kennedy Space Center, FL.Google Scholar
Woodhouse, R., Heeb, M., Berry, W., Hoshizaki, T. & Wood, M. (1994). Analysis of remote reflection spectroscopy to monitor plant health. Adv. Space Res. 14, 199–202.CrossRefGoogle ScholarPubMed