SpringerLink
Log in

Hyperspectral remote sensing of grapevine drought stress

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

In karst landscapes stony soils have little water holding capacity; the rational use of water for irrigation therefore plays an important management role. Because the water holding capacity is not homogenous, precision agriculture approaches would enable better management decisions. This research was carried out in an experimental vineyard grown in an artificially transformed karst terrain in Dalmatia, Croatia. The experimental design included four water treatments in three replicates: (1) fully irrigated, based on 100% crop evapotranspiration (ETc) application (N100); (2 and (3) deficit irrigation, based on 75% and 50% ETc applications (N75 and N50, respectively); and (4) non-irrigated (N0). Hyperspectral images of grapevines were taken in the summer of 2016 using two spectral-radiance (W sr−1 m−2) calibrated cameras, covering wavelengths from 409 to 988 nm and 950 to 2509 nm. The four treatments were grouped into a new set consisting of: (1) drought (N0); and (2) irrigated (the remaining three treatments: N100, N75, and N50). The images were analyzed using Partial Least Squares-Discriminant Analysis (PLS-DA), and treatments were classified using PLS-Single Vector Machines (PLS-SVM). PLS-SVM demonstrated the capability to determine levels of grapevine drought or irrigated treatments with an accuracy of more than 97%. PLS-DA identified relevant wavelengths, which were linked to O–H, C–H, and N–H stretches in water, carbohydrates and proteins. The study presents the applicability of hyperspectral imaging for drought stress assessment in grapevines, even though temporal variability needs to be taken into account for early detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration—guidelines for computing crop water requirements—FAO Irrigation and drainage paper 56. Rome: Food and Agriculture Organization.

    Google Scholar 

  • Al-Yahyai, R., Schaffer, B., Davies, F. S., & Muñoz-Carpena, R. (2006). Characterization of soil–water retention of a very gravelly loam soil varied with determination method. Soil Science, 17, 85–93.

    Article  CAS  Google Scholar 

  • Ashraf, M. (2009). Inducing drought tolerance in plants. Biotechnology Advances, 28(1), 169.

    Article  CAS  Google Scholar 

  • Ballabio, D., & Consonni, V. (2013). Classification tools in chemistry. Part I: Linear models. PLS-DA. Analytical Methods, 5, 3790–3798.

    Article  CAS  Google Scholar 

  • Behman, J., Steinrücken, J., & Plümer, L. (2014). Detection of early plant stress responses in hyperspectral images. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 98–111.

    Article  Google Scholar 

  • Belousov, A. I., Verzakov, S. A., & von Frese, J. (2002). Applicational aspects of support vector machines. Journal of Chemometrics, 16, 482–489.

    Article  CAS  Google Scholar 

  • Cao, Q., Zhegalova, N. G., Wang, S. T., Akers, W. J., & Berezin, M. Y. (2013). Multispectral imaging in the extended near-infrared window based on endogenous chromophores. Journal of Biomedical Optics, 18(10), 101318.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chaves, M. M., Flexas, J., & Pinheiro, C. (2009). Photosynthesis under drought and salt stress: Regulation mechanisms from whole plant to cell. Annals of Botany, 103(4), 551–560.

    Article  CAS  PubMed  Google Scholar 

  • Chaves, M. M., Maroco, J. P., & Pereira, J. S. (2003). Understanding plant responses to drought—From genes to the whole plant. Functional Plant Biology, 30(3), 239–264.

    Article  CAS  Google Scholar 

  • Chong, I. G., & Jun, C. H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78, 103–112.

    Article  CAS  Google Scholar 

  • Clevers, J. G. P. W., Kooistra, L., & Schaepman, M. E. (2010). Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 12, 119–125.

    Article  Google Scholar 

  • Coppola, A., Dragonetti, G., Comegna, A., Lamaddalena, N., Caushi, B., Haikal, M. A., et al. (2013). Measuring and modeling water content in stony soils. Soil & Tillage Research, 128, 9–22.

    Article  Google Scholar 

  • Diago, M. P., Pou, A., Millan, B., Tardaguila, J., Fernandes, A. M., & Melo-Pinto, P. (2014). Assessment of grapevine water status from hyperspectral imaging of leaves. Acta Horticulturae (ISHS), 1038, 89–96.

    Article  Google Scholar 

  • Du, Y., Chang, C. I., Ren, H., Chang, C. C., & Jensen, J. O. (2004). New hyperspectral discrimination measure for spectral characterization. Optical Engineering, 43(8), 1777–1786.

    Article  Google Scholar 

  • Elsayed, S., Mistele, B., & Schmidhalter, U. (2011). Can changes in leaf water potential be assessed spectrally? Functional Plant Biology, 38, 523–533.

    Article  Google Scholar 

  • Fiorani, F., Rascher, U., Jahnke, S., & Schurr, U. (2012). Imaging plants dynamics in heterogenic environments. Current Opinion in Biotechnology, 23(2), 227–235.

    Article  CAS  PubMed  Google Scholar 

  • Girona, J., Mata, M., del Campo, J., Arbonés, A., Bartra, E., & Marsal, J. (2006). The use of midday leaf water potential for scheduling deficit irrigation in vineyards. Irrigation Science, 24(2), 115–127. https://doi.org/10.1007/s00271-005-0015-7.

    Article  Google Scholar 

  • Govender, M., Dye, P. J., Weiersbye, I. M., Witkowski, E. T. F., & Ahmed, F. (2009). Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA, 35, 741–752.

    Article  Google Scholar 

  • Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K., & Lehman, B. L. (2011). Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture, 77(2), 155–160.

    Article  Google Scholar 

  • Kim, D. M., Zhang, H., Zhou, H., Du, T., Wu, Q., Mockler, T. D., et al. (2015). Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis. Scientific Reports, 5, 15919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li, X., Sun, C., Zhou, B., & He, Y. (2015). Determination of hemicellulose, cellulose and lignin in Moso bamboo by near infrared spectroscopy. Scientific Reports, 5, 17210. https://doi.org/10.1038/srep17210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mahesh, S., Manickavasagan, A., Jayas, D. S., Paliwal, J., & White, N. D. G. (2008). Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering, 101(1), 50–57.

    Article  Google Scholar 

  • Mahlein, A.-K., Steiner, U., Hillnhütter, C., Dehne, H.-W., & Oerke, E.-C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods, 8, 3.

    Article  PubMed  PubMed Central  Google Scholar 

  • Marsal, J., Mata, M., Arbones, A., Del Campo, J., Girona J., & Lopez , G. (2008). Factors involved in alleviating water stress by partial crop removal in pear trees. Tree Physiology, 28, 1375–1382.

    Article  PubMed  Google Scholar 

  • Matese, A., & Di Genaro, S. F. (2014). Technology in precision viticulture: a state of the art review. International Journal of Wine Research, 7, 69–81.

    Google Scholar 

  • Mehuys, G. R., Stolzy, L. H., & Letey, J. (1975). Temperature distributions under stones submitted to a diurnal heat wave. Soil Science, 120, 437–441.

    Article  Google Scholar 

  • Moss, R. A., & Loomis, W. E. (1952). Absorption spectra of leaves. Plant Physiology, 27(2), 370–391.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Munné-Bosch, S., & Alegre, L. (2004). Die and let live: leaf senescence contributes to plant survival under drought stress. Functional Plant Biology, 31(3), 203–216.

    Article  Google Scholar 

  • Pennisi, E. (2008). The blue revolution, drop by drop, gene by gene. Science, 320(5873), 171–173.

    Article  CAS  PubMed  Google Scholar 

  • Piqueras, S., Burger, J., Tauler, R., & de Juan, A. (2012). Relevant aspects of quantification and sample heterogeneity in hyperspectral image resolution. Chemometrics and Intelligent Laboratory Systems, 117, 169–182.

    Article  CAS  Google Scholar 

  • Qiao, J., Ngadi, M. O., Wang, N., GariePy, C., & Prasher, S. O. (2007). Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering, 83, 10–16.

    Article  Google Scholar 

  • R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. https://www.R-project.org/.

  • Ravikanth, L., Singh, C. B., Jayas, D. S., & White, N. D. G. (2015). Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering, 135, 73–86.

    Article  Google Scholar 

  • Rodriguez-Perez, J. R., Riano, D., Carlisle, E., Ustin, S., & Smart, D. R. (2007). Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. American Journal of Enology and Viticulture, 58(3), 302–317.

    Google Scholar 

  • Römer, C., Wahabzada, M., Ballvora, A., Pinto, F., Rossini, M., Panigada, C., et al. (2012). Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Functional Plant Biology, 39(11), 878–890.

    Article  Google Scholar 

  • Romic, M., Zovko, M., Romic, D., & Bakic, H. (2012). Improvement of vineyard management of Vitis vinifera L cv Grk in the Lumbarda Vineyard region (Croatia). Communications in Soil Science and Plant Analysis, 43, 209–218. https://doi.org/10.1080/00103624.2011.638557.

    Article  CAS  Google Scholar 

  • Rumpf, T., Mahlein, K., Steiner, A., Oerke, U., Dehne, H., & Pluemer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and electronics in agriculture, 74(1), 91–99.

    Article  Google Scholar 

  • Steele, M. R., Gitelson, A. A., Rundquist, D. C., & Merzlyak, M. N. (2009). Nondestructive estimation of anthocyanin content in grapevine leaves. American Journal of Enology and Viticulture, 60, 87–92.

    CAS  Google Scholar 

  • Susič, N., Žibrat, U., Širca, S., Strajnar, P., Razinger, J., Knapič, M., et al. (2018). Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging. Sensors and actuators B: Chemical, 273, 842–852. https://doi.org/10.1016/j.snb.2018.06.121.

    Article  CAS  Google Scholar 

  • Vadez, V., Kholova, J., Choudhary, S., Zindy, P., Terrier, M., Krishnamurth, L., et al. (2011). Responses to increased moisture stress and extremes: Whole plant response to drought under climate change. In S. S. Yadav, R. Redden, J. L. Hatfield, H. Lotze-Campen, & A. E. Hall (Eds.), Crop adaptation to climate change (pp. 186–197). Orford: Chichester-Wiley-Blackwell.

    Chapter  Google Scholar 

  • Vigneau, N., Ecarnot, M., Rabatel, 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(1), 25–31.

    Article  Google Scholar 

  • Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., & Kersting, K. (2015). Metro maps of plant disease dynamics automated mining of differences using hyperspectral images. PLoS ONE, 10(1), e0116902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., & Kersting, K. (2016). Plant phenoty** using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific Reports, 6, 22482.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • **ménez-Embún, M. G., Ortego, F., & Castañera, P. (2016). Drought-stressed tomato plants trigger bottom-up effects on the invasive Tetranychus evansi. PLoS ONE, 11, e0145275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yin, W., Zhang, C., Zhu, H., Zhao, Y., & He, Y. (2017). Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries. PLoS ONE, 12, e0180534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zarco-Tejada, P. J., Berjon, A., Lopez-Lozano, R., Miller, J. R., Martın, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99, 271–287.

    Article  Google Scholar 

  • Zovko, M., Žibrat, U., Knapič, M., Bubalo, M., Romić, M., & Romić, D. (2017). Hyperspectral imagery as a supporting tool in precision irrigation of karst landscapes. Advances in Animal Biosciences, 8(2), 578–582. https://doi.org/10.1017/S2040470017000991.

    Article  Google Scholar 

Download references

Acknowledgments

This research has been partially supported in part by Croatian Science Foundation under the Project IP-2016-06-8379, SENSIRRIKA - Advanced sensor systems for precision irrigation in karst landscape, and partially by the Croatian Waters, and by the Slovenian Research Agency (ARRS), Grant P4-0072.

Author information

Authors and Affiliations

  1. Faculty of Agriculture, University of Zagreb, Zagreb, Croatia

    M. Zovko, M. Bubalo Kovačić & D. Romić

  2. Agricultural Institute of Slovenia, Ljubljana, Slovenia

    U. Žibrat & M. Knapič

Authors
  1. M. Zovko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. U. Žibrat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Knapič
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Bubalo Kovačić
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. D. Romić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Zovko.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zovko, M., Žibrat, U., Knapič, M. et al. Hyperspectral remote sensing of grapevine drought stress. Precision Agric 20, 335–347 (2019). https://doi.org/10.1007/s11119-019-09640-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-019-09640-2

Keywords

Search

Navigation