Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T00:24:21.517Z Has data issue: false hasContentIssue false
  • English
  • Français

Hyperspectral imagery as a supporting tool in precision irrigation of karst landscapes

Published online by Cambridge University Press:  01 June 2017

M. Zovko ,
U. Žibrat ,
M. Knapič ,
M. Bubalo ,
M. Romić  and
D. Romić
M. Zovko*
Affiliation:
University of Zagreb, Faculty of Agriculture, Zagreb, Croatia
U. Žibrat
Affiliation:
The Agricultural Institute of Slovenia, Ljubljana, Slovenia
M. Knapič
Affiliation:
The Agricultural Institute of Slovenia, Ljubljana, Slovenia
M. Bubalo
Affiliation:
University of Zagreb, Faculty of Agriculture, Zagreb, Croatia
M. Romić
Affiliation:
University of Zagreb, Faculty of Agriculture, Zagreb, Croatia
D. Romić
Affiliation:
University of Zagreb, Faculty of Agriculture, Zagreb, Croatia
*
†E-mail: [email protected]
Get access

Abstract

This research was carried out in an experimental vineyard grown in artificially transformed karst terrain (Croatia). The experimental design included four water treatments in three replicates: 1) fully watered or based on 100% evapotranspiration (ETc) application; 2) regulated deficit irrigation based on 75% and 50% ETc applications; and 3) non-watered. 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 two new sets: 1) drought (NO); and 2) watered (the remaining three treatments). The watered group was then split into two new sets: 1) 50% treatment; and 2) 75+100% treatment. The images were analyzed using Partial least squares – discriminant analysis (PLS-DA), and Single Vector Machines (SVM). The PLS-DA demonstrated the capability to determine levels of grapevine drought or watered groups with 70 - 80% accuracy. A similar success rate was achieved in distinguishing the 50% group from the 75+100% group.

Keywords

vineyardirrigationwater stresshyperspectral imagerysoil
Type
Precision Irrigation
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 578 - 582
Copyright
© The Animal Consortium 2017 

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

Al-Yahyai, R, Schaffer, B, Davies, FS and Muñoz-Carpena, R 2006. Characterization of soil-water retention of a very gravelly loam soil varied with determination method. Soil Science 17, 85–93.CrossRefGoogle Scholar
Cao, Q, Zhegalova, NG, Wang, ST, Akers, WJ and Berezin, MY 2013. Multispectral imaging in the extended near-infrared window based on endogenous chromophores. Journal of Biomedical Optics 18 (10), 101318.CrossRefGoogle ScholarPubMed
Clevers, JGPW, Kooistra, L and Schaepman, ME 2010. Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation 12, 119–125.CrossRefGoogle Scholar
Diago, MP, Pou, A, Millan, B, Tardaguila, J, Fernandes, AM and Melo-Pinto, P 2014. Assessment of grapevine water status from hyperspectral imaging of leaves. Acta Hort. (ISHS) 1038, 89–96.Google Scholar
Elsayed, S, Mistele, B and Schmidhalter, U 2011. Can changes in leaf water potential be assessed spectrally? Functional Plant Biology 38, 523–533.CrossRefGoogle ScholarPubMed
Govender, M, Dye, PJ, Weiersbye, IM, Witkowski, ETF and Ahmed, F 2009. Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA 35, 741–752.CrossRefGoogle Scholar
Genc, L, Inalpulat, M, Kizil, U, Mirik, M, Smith, ST and Mendes, M 2013. Determination of water stress with spectral reflectance on sweet corn (Zea mays L.) using classification tree (CT) analysis. Zemdirbyste-Agriculture 100 (1), 81–90.CrossRefGoogle Scholar
Hunt, RE, Rock, BN and Nobel, PS 1987. Measurement of leaf relative water content by infrared reflectance. Remote Sensing of the Environment 22, 429–435.CrossRefGoogle Scholar
Kim, DM, Zhang, H, Zhou, H, Du, T, Wu, Q, Mockler, TD and Berezin, MY 2015. Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis. Scientific Reports 5, 15919.Google Scholar
Li, L, Ustin, SL and Lay, M 2005. Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico. Remote Sensing of the Environment 94, 1–16.CrossRefGoogle Scholar
Mahesh, S, Manickavasagan, A, Jayas, DS, Paliwal, J and White, NDG 2008. Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering 101 (1), 50–57.Google Scholar
Matese, A and Di Genaro, SF 2014. Technology in precision viticulture, a state of the art review.Google Scholar
Piqueras, S, Burger, J, Tauler, R and de Juan, A 2012. Relevant aspects of quantification and sample heterogeneity in hyperspectral image resolution. Chemometrics and Intelligent Laboratory Systems 117, 169–182.CrossRefGoogle Scholar
Ravikanth, L, Singh, CB, Jayas, DS and White, NDG 2015. Classification of contaminants from wheat using near-infrared hyperspectral imaging. Biosystems Engineering 135, 73–86.Google Scholar
Rodriguez-Perez, JR, Riano, D, Carlisle, E, Ustin, S and Smart, DR 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
Zarco-Tejada, PJ, Berjon, A, Lopez-Lozano, R, Miller, JR, Martın, P, Cachorro, V, Gonzalez, MR and de Frutos, A 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.Google Scholar