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Precision Agriculture

, Volume 18, Issue 3, pp 332–349 | Cite as

Crop water status assessment in controlled environment using crop reflectance and temperature measurements

  • A. Elvanidi
  • N. Katsoulas
  • T. Bartzanas
  • K. P. Ferentinos
  • C. KittasEmail author
Article

Abstract

Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called ‘speaking plant’ and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590–680, 690–830 and 830–1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490–510, 530–560, 660–670 and 730–760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last being extensively used for crop water status assessment in greenhouses and open field. Thus, it could be concluded that reflectance and crop temperature measurements might be combined to provide alarm signals when crop water status reaches critical levels for optimal plant growth.

Keywords

Remote sensing Speaking plant approach Multispectral camera Crop water stress index 

Notes

Acknowledgements

This work has been co-financed by the European Union and Greek National Funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—ARISTEIA “GreenSense” project. It was also supported by the EU Seventh Framework Programme, within the project “Smart Controlled Environment Agriculture Systems—Smart-CEA”, contract number (PIRSES-GA-2010-269240), which is carried out under the Marie Curie Actions: People—International Research Staff Exchange Scheme. Funding was provided by General Secretariat for Research and Technology (14289/12.12.2013).

References

  1. Alchanatis, V., Cohen, Y., Cohen, S., Moller, M., Sprinstin, M., & Meron, M. (2010). Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging. Precision Agriculture, 11, 27–41. doi: 10.1007/s11119-009-9111-7.CrossRefGoogle Scholar
  2. Amatya, S., Karkee, M., Alva, A. K., Larbi, P., & Adhikari, B. (2012). Hyperspectral imaging for detecting water stress in potatoes. Annual international meeting sponsored by ASABE, Paper Number 12-1345197.Google Scholar
  3. American National Standard. (1998). Standard test methods for measuring and compensating for reflected temperature using infrared imaging radiometers. American Society for Testing and Materials Licensed, E1862-97.Google Scholar
  4. Baille, A., Kittas, V., & Katsoulas, N. (2001). Influence of whitening on greenhouse microclimate and crop energy. Agricultural and Forest Meteorology, 107, 293–306.CrossRefGoogle Scholar
  5. Bowman, W. D., Hubick, K. T., Von Caemmerer, S., & Farquhar, G. D. (1989). Short-term changes in leaf carbon isotope discrimination in salt- and water-stressedgrasses. Plant Physiology, 90, 162–166.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Chaves, M. M., Pereira, J. S., Maroco, J., Rodrigues, M. L., Ricardo, C. P. P., & Osόrio, M. L. (2002). How plants cope with water stress in the field: photosynthesis and growth. Annals of Botany, 89, 907–916.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Chavez, J. L., Pierce, F. J., Elliott, T. V., & Evans, R. G. (2010). A remote irrigation monitoring and control system for continuous move systems. Part A: Description and development. Precision Agriculture, 11, 1–10. doi: 10.1007/s11119-009-9109-1.CrossRefGoogle Scholar
  8. Clawson, K. L., Jackson, R. D., & Pinter, P. J. (1989). Evaluating plant water stress with canopy temperature differences. Agronomy Journal, 81, 858–863.CrossRefGoogle Scholar
  9. Cohen, Y., Alchanatis, V., Sela, E., Saranga, Y., Cohen, S., Meron, M., et al. (2015). Crop water status estimation using thermography: Multi-year model development using ground-based thermal images. Precision Agriculture, 16, 311–329.CrossRefGoogle Scholar
  10. Cordon, G. B., & Lagorio, M. G. (2007). Absorption and scattering coefficients: A biophysical-chemistry experiment using reflectance spectroscopy. Journal of Chemical Education, 84(7), 1167–1170.CrossRefGoogle Scholar
  11. Genc, L., Demirel, K., Camoglu, G., Asik, S., & Smith, S. (2011). Determination of plant water stress using spectral reflectance measurements in watermelon (Citrullus vulgaris). American-Eurasian Journal Agriculture & Environment Science, 11, 296–304.Google Scholar
  12. González-Dugo, V., Zarco-Tejada, P., Nicolas, E., Nortes, P. A., Alarcon, J. J., & Intrigliolo, D. S. (2013). Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture, 14, 660–678. doi: 10.1007/s11119-013-9322-9.CrossRefGoogle Scholar
  13. Hunt, E. R., & Rock, B. N. (1989). Detection of change in leaf water content using near and middle infrared reflectance. Remote Sensing of Environment, 30, 43–54.CrossRefGoogle Scholar
  14. Jackson, R. D., Idso, S. B., Reginato, R. J., & Pinter, P. J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 171, 133–138.Google Scholar
  15. Jacquemoud, S., & Ustin, S. L. (2008). Modeling leaf optical properties. Photo biologica lsciences online. American Society for Photobiology, from http://photobiology.info/
  16. Jacquemoud, S., Verhoel, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., et al. (2009). Prospect + sail models: A review of use for vegetation characterization. Remote Sensing of Environment, 113, S56–S66.CrossRefGoogle Scholar
  17. Jain, N., Ray, S. S., Singh, J. P., & Panigrahy, S. (2007). Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop. Precision Agriculture, 8, 225–239.CrossRefGoogle Scholar
  18. Jones, H. G., & Schofield, P. (2008). Thermal and other remote sensing of plant stress. General and Applied Plant Physiology, 34, 19–32.Google Scholar
  19. Jones, C. L., Weckler, P. R., Maness, N. O., Jayasekara, R., Stone, M. L., & Chrz, D. (2007). Remote sensing to estimate chlorophyll concentration in spinach using multi-spectral plant reflectance. American Society of Agricultural and Biological Engineers, 50(6), 2267–2273.Google Scholar
  20. Jones, C. L., Weckler, P. R., Maness, N. O., Stone, M. L., & Jayasekara, R. (2004). Estimating water stress in plant using hyperspectral sensing. Annual International Meeting Sponsored By ASAE/CSAE, Paper Number 043065.Google Scholar
  21. Kacira, M., Ling, P. P., & Short, T. H. (2002). Establishing crop water stress index (CWSI) threshold values for early, non-contact detection of plant water stress. Transactions of the ASAE, 45, 775–780.Google Scholar
  22. Kacira, M., Sase, S., Okushima, L., & Ling, P. P. (2005). Plant response-based sensing for control strategies in sustainable greenhouse production. Journal Agricultural Meteorology, 61, 15–22.CrossRefGoogle Scholar
  23. Katsoulas, N., Baille, A., & Kittas, C. (2001). Effect of misting on transpiration and conductances of a greenhouse rose canopy. Agricultural and Forest Meteorology, 106, 233–247.CrossRefGoogle Scholar
  24. Katsoulas, N., Baille, A., & Kittas, C. (2002). Influence of leaf area index on canopy energy partitioning and greenhouses cooling requirements. Biosystems Engineering, 83(3), 349–359.CrossRefGoogle Scholar
  25. Katsoulas, N., Elvanidi, A., Ferentinos, K.P., Bartzanas, T., & Kittas, C. (2016). Calibration methodology of a hyperspectral imaging system for greenhouse plant water stress estimation. Acta Horticulturae, 1142, 119-126.CrossRefGoogle Scholar
  26. Katsoulas, N., Savas, D., Tsirogiannis, I., Merkouris, O., & Kittas, C. (2009). Response of an eggplant crop grown under Mediterranean summer conditions to greenhouse fog cooling. Scientia Horticulturae, 123, 90–98.CrossRefGoogle Scholar
  27. Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K., & Lehman, B. L. (2010). Hyperspectral image analysis for plant stress detection (p. 1009114). Paper Number: Annual International Meeting.Google Scholar
  28. Knipling, E. B. (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1, 155–159.CrossRefGoogle Scholar
  29. Köksal, E. S. (2011). Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicators. Agricultural Water Management, 98, 1317–1328.CrossRefGoogle Scholar
  30. Köksal, E. S., Güngör, Y., & Yildirim, Y. E. (2010). Spectral reflectance characteristics of sugar beet under different levels of irrigation water and relationships between growth parameters and spectral indexes. Irrigation and Drainage, 60, 187–195.CrossRefGoogle Scholar
  31. Kruse, J. K. (2004). Remote sensing of moisture and nutrient stress. Dissertation, Ioawa State of University, Ames.Google Scholar
  32. Ling, P. P., Giacomelli, G. A., & Russell, T. P. (1996). Monitoring of plant development in controlled environment with machine vision. Advances in Space Research, 18(4–5), 101–112.CrossRefPubMedGoogle Scholar
  33. Liu, L., Wang, J., Huang, W., Zhao, C., Zhang, B., & Tong, Q. (2004). Estimating winter wheat plant water content using red edge parameters. International Journal of Remote Sensing, 25(17), 3331–3342.CrossRefGoogle Scholar
  34. Meron, M., Tsipris, J., Orlov, V., Alchanatis, V., & Cohen, Y. (2010). Crop water stress mapping for site-specific irrigation by thermal imagery and artificial reference surfaces. Precision Agriculture, 11, 148–162. doi: 10.1007/s11119-009-9153-x.CrossRefGoogle Scholar
  35. Norikane, J. H., & Kurata, K. (2001). Water stress detection by monitoring fluorescence of plants under ambient light. Transactions of the ASAE, 44, 1915–1922.CrossRefGoogle Scholar
  36. O’Shaughnessy, S. A., Hebel, M. A., Evett, S. R., & Colaizzi, P. D. (2011). Evaluation of wireless infrared thermometer with a narrow field of view. Computer and Electronics in Agriculture, 76, 59–68.CrossRefGoogle Scholar
  37. Peňuelas, J., & Inoue, Y. (1999). Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica, 36, 355–360.CrossRefGoogle Scholar
  38. Peňuelas, J., Pinol, J., Ogaya, R., & Filella, I. (1997). Estimation of plant water content by the reflectance water index WI(R900/R970). International Journal of Remote Sensing, 18, 2869–2875.CrossRefGoogle Scholar
  39. Ray, S. S., Das, G., Singh, J. P., & Panigrahy, S. (2006). Evaluation of hyperspectralindices for LAI estimation and discrimination of potato crop under different irrigation treatments. International Journal of Remote Sensing, 27, 5373–5387.CrossRefGoogle Scholar
  40. Sarlikioti, V., Driever, S. M., & Marcellis, L. F. M. (2010). Photochemical reflectance index as a mean of monitoring early water stress. Annals of Applied Biology, 157, 81–89. doi: 10.1111/j.1744-7348.2010.00411.x.CrossRefGoogle Scholar
  41. Sclemmer, M. R., Francis, D. D., Shanahan, J. F., & Scepers, J. S. (2005). Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agronomy and Horticulture, 97, 106–112.Google Scholar
  42. Shimada, S., Funatsuka, E., Ooda, M., & Takyu, M. (2012). Developing the monitoring method for plant water stresss. Journal or Arid Land Studies, 22, 251–254.Google Scholar
  43. Story, D., & Kacira, M. (2015). Design and implementation of a computer vision-guided greenhouse crop diagnostics system. Machine Vision and Applications, 26, 495–506.CrossRefGoogle Scholar
  44. Suárez, L., Zarco-Tejada, P. J., Berni, A. J., González-Dugo, V., & Fereres, E. (2009). Modelling PRI for water stress detection using radiative transfer models. Remote Sensing of Environment, 113, 730–744.CrossRefGoogle Scholar
  45. Taiz, L., Zeiger, E., Møller, I. M., & Murphy, A. (2015). Plant physiology and development (6th ed.). Washington: Sinauer Associates.Google Scholar
  46. Takakura, T. (1974). Greenhouse production in engineering aspects. In Y. Hashimoto & W. Day (Eds.), Mathematical and control applications in agriculture and horticulture. Oxford: Pergamon Press.Google Scholar
  47. Tsirogiannis, I. L., Katsoulas, N., Savvas, D., Karras, G., & Kittas, C. (2013). Relationship between reflectance and water status in a greenhouse rocket (Erucasativa mill.) cultivation. European Journal of Horticultural Science, 78, 275–282.Google Scholar
  48. Tuominen, J. & Lipping, T. 2011. Detection of environmental changes using hyperspectral remote sensing at Olkiluoto repository site. Working Report 2011-26, Posiva, OY, Finland.Google Scholar
  49. Verdebout, J., Jacquemoud, S., & Schmuck, G. (1994). Optical properties of leaves: Modeling and experimental studies. In J. Hill & J. Megier (Eds.), A tool for environmental observations (pp. 169–191). Brussels: Springer.Google Scholar
  50. Vigneau, N., Ecarnotb, M., Rabatela, G., & Roumet, P. (2011). Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Research, 122, 25–31.CrossRefGoogle Scholar
  51. Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8), 1563–1575.CrossRefGoogle Scholar
  52. Zakaluk, R., & Sri Ranjan, R. (2007). Artificial neural network modelling of leaf water potential for potatoes using RGB digital images: A greenhouse study. Potato Research, 49, 255–272.CrossRefGoogle Scholar
  53. Zakaluk, R., & Sri Ranjan, R. (2008). Predicting the leaf water potential of potato plants using RGB reflectance. Canadian Biosystems Engineering Journal, 50, 7.1–7.12.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • A. Elvanidi
    • 1
    • 2
  • N. Katsoulas
    • 1
    • 2
  • T. Bartzanas
    • 2
  • K. P. Ferentinos
    • 2
  • C. Kittas
    • 1
    • 2
    Email author
  1. 1.Department of Agriculture Crop Production and Rural EnvironmentUniversity of ThessalyVólosGreece
  2. 2.Centre for Research and Technology HellasInstitute for Research and Technology of ThessalyVólosGreece

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