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Automatic discrimination of grapevine (Vitis vinifera L.) clones using leaf hyperspectral imaging and partial least squares

Published online by Cambridge University Press:  08 April 2014

A. M. FERNANDES
Affiliation:
CITAB – Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-911, Vila Real, Portugal
P. MELO-PINTO
Affiliation:
CITAB – Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-911, Vila Real, Portugal Department of Engineering, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-911, Vila Real, Portugal
B. MILLAN
Affiliation:
Instituto de Ciencias de la Vid y del Vino (University of La Rioja, CSIC, Gobierno de La Rioja), C/Madre de Dios, 51. 26006 Logroño, Spain
J. TARDAGUILA
Affiliation:
Instituto de Ciencias de la Vid y del Vino (University of La Rioja, CSIC, Gobierno de La Rioja), C/Madre de Dios, 51. 26006 Logroño, Spain
M. P. DIAGO*
Affiliation:
Instituto de Ciencias de la Vid y del Vino (University of La Rioja, CSIC, Gobierno de La Rioja), C/Madre de Dios, 51. 26006 Logroño, Spain Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
*
*To whom all correspondence should be addressed. Email: mpaz.diago.santamaria@gmail.com

Summary

A worldwide innovative method to discriminate grapevine clones is presented. It is an alternative to ampelography, isozyme and DNA analysis. The spectra and their first and second derivatives of 201 bands in the visible and near-infrared wavelength range between 634 and 759 nm were used as inputs to a classifier created using partial least squares. The spectra were acquired in the laboratory for the adaxial side of the apical part of the main lobe of fully hydrated grapevine leaves. The classifier created allowed the separation of 100 leaves of the Cabernet Sauvignon (Vitis vinifera L.) variety into four clones, namely CS 15, CS 169, CS 685 and CS R5, comprising 25 leaves each. The percentages of leaves correctly classified for these clones were 98·2, 99·2, 100 and 97·8%, respectively, when the classifier input was the second derivative of the normalized spectra. These percentages were determined by Monte-Carlo cross-validation. With the new method proposed, each leaf of a given variety can be classified in a few seconds according to its clone in an environmentally friendly way.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Alonso-Villaverde, V., Boso, S., Santiago, J. L., Gago, P., Rodriguez-Garcia, M. I. & Martinez, M. C. (2011). Leaf thickness and structure of Vitis vinifera L. CV. albarino clones and its possible relation with susceptibility to downy mildew (Plasmopara viticola) infection. Journal International des Sciences de la Vigne et du Vin 45, 161169.Google Scholar
Anhalt, U. C. M., Martinez, S. C., Ruhl, E. & Forneck, A. (2011). Dynamic grapevine clones-an AFLP-marker study of the Vitis vinifera cultivar Riesling comprising 86 clones. Tree Genetics and Genomes 7, 739746.Google Scholar
Arlot, S. & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys 4, 4079.Google Scholar
Asner, G. P., Jones, M. O., Martin, R. E., Knapp, D. E. & Hughes, R. F. (2008). Remote sensing of native and invasive species in Hawaiian forests. Remote Sensing of Environment 112, 19121926.CrossRefGoogle Scholar
Blaich, R., Konradi, J., Rühl, E. & Forneck, A. (2007). Assessing genetic variation among Pinot noir (Vitis vinifera L.) clones with AFLP markers. American Journal of Enology and Viticulture 58, 526529.CrossRefGoogle Scholar
Burkholder, A., Warner, T. A., Culp, M. & Landenberger, R. (2011). Seasonal trends in separability of leaf reflectance spectra for Ailanthus altissima and four other tree species. Photogrammetric Engineering and Remote Sensing 77, 793804.Google Scholar
Butt, R. (2010). Introduction to Numerical Analysis using MATLAB. Sudbury, MA: Jones and Bartlett Publishers.Google Scholar
Castro-Esau, K. L., Sánchez-Azofeifa, G. A. & Caelli, T. (2004). Discrimination of lianas and trees with leaf-level hyperspectral data. Remote Sensing of Environment 90, 353372.CrossRefGoogle Scholar
Cervera, M. T., Cabezas, J. A., Sanchez-Escribano, E., Cenis, J. L. & Martinez-Zapater, J. M. (2000). Characterization of genetic variation within table grape varieties (Vitis vinifera L.) based on AFLP markers. Vitis: Journal of Grapevine Research 39, 109114.Google Scholar
Collins, W. (1978). Remote sensing of crop type and maturity. Photogrammetric Engineering and Remote Sensing 44, 4355.Google Scholar
Conforto, E. C., Bittencourt, N. S. Jr., Scaloppi, E. J. Jr. & Moreno, R. M. B. (2011). Comparação entre folhas sombreadas de sete clones adultos de seringueira. Revista Ceres 58, 2934.CrossRefGoogle Scholar
Cretazzo, E., Meneghetti, S., De Andrés, M. T., Gaforio, L., Frare, E. & Cifre, J. (2010). Clone differentiation and varietal identification by means of SSR, AFLP, SAMPL and M-AFLP in order to assess the clonal selection of grapevine: the case study of Manto Negro, Callet and Moll, autochthonous cultivars of Majorca. Annals of Applied Biology 157, 213227.Google Scholar
Durgante, F. M., Higuchi, N., Almeida, A. & Vicentini, A. (2013). Species spectral signature: discriminating closely related plant species in the Amazon with near-infrared leaf-spectroscopy. Forest Ecology and Management 291, 240248.Google Scholar
EHE (2006). Spectral Calibration with Osram Dulux Mobil Fluorescence Lamp. Technical Note TN-0014. Oulu, Finland: SPECIM- Spectral Imaging Ltd.Google Scholar
Engels, J. M. M. (1983). A systematic description of cacao clones. 2. The discriminative value of qualitative characteristics and the practical compatibility of the discriminative value of quantitative and qualitative descriptors. Euphytica 32, 387396.Google Scholar
Fanizza, G., Lamaj, F., Resta, P., Ricciardi, L. & Savino, V. (2005). Grapevine cvs Primitivo, zinfandel and Crljenak kastelanski: molecular analysis by AFLP. Vitis: Journal of Grapevine Research 44, 147148.Google Scholar
Ferrandino, A. & Guidoni, S. (2010). Anthocyanins, flavonols and hydroxycinnamates: an attempt to use them to discriminate Vitis vinifera L. cv ‘Barbera’ clones. European Food Research and Technology 230, 417427.Google Scholar
Ferreiro-Armán, M., Da Costa, J. P., Homayouni, S. & Martin-Herrero, J. (2006). Hyperspectral image analysis for precision viticulture. Lecture Notes in Computer Science 4142, 730741.CrossRefGoogle Scholar
Galet, P. (1979). A Practical Ampelography: Grapevine Identification. Ithaca, NY: Comstock Pub. Associates.Google Scholar
Gausman, H. W., Allen, W. A., Cardenas, R. & Richardson, A. J. (1970). Relation of light reflectance to histological and physical evaluations of cotton leaf maturity. Applied Optics 9, 545552.CrossRefGoogle ScholarPubMed
Gayol, M. F., Labuckas, D. O., Nicotra, V. E., Oberti, J. C. & Guzman, C. A. (2009). Simmondsins quantification by spectrophotometry UV-vis in press cake and in jojoba seeds (Simmondsia chinensis (Link) Schneider), from Argentina. Industrial Crops and Products 29, 177181.CrossRefGoogle Scholar
Geladi, P. & Kowalski, B. R. (1986). Partial least-squares regression – a tutorial. Analytica Chimica Acta 185, 117.CrossRefGoogle Scholar
Grant, L. (1987). Diffuse and specular characteristics of leaf reflectance. Remote Sensing of Environment 22, 309322.Google Scholar
Imazio, S., Labra, M., Grassi, F., Winfield, M., Bardini, M. & Scienza, A. (2002). Molecular tools for clone identification: the case of the grapevine cultivar ‘Traminer’. Plant Breeding 121, 531535.Google Scholar
Konradi, J., Blaich, R. & Forneck, A. (2007). Genetic variation among clones and sports of ‘Pinot noir’ (Vitis vinifera L.). European Journal of Horticultural Science 72, 275279.Google Scholar
Kozjak, P., Korošec-Koruza, Z. & Javornik, B. (2003). Characterization of cv. Refošk (Vitis vinifera L.) by SSR markers. Vitis: Journal of Grapevine Research 42, 8386.Google Scholar
Lacar, F. M., Lewis, M. M. & Grierson, I. T. (2001). Use of hyperspectral imagery for mapping grape varieties in the Barossa valley, South Australia. In Geoscience and Remote Sensing Symposium (2001-IGARSS ‘01), 9–13 July 2001, Sydney, Australia, Vol. 6 (Ed. IEEE), pp. 28752877. Piscataway, NJ: IEEE.Google Scholar
Lin, D.-D., Zhang, G.-F., Yu, J.-B. & Feng, J. (2011). Analyses of photosynthetic pigment content and chlorophyll fluorescence parameter in leaves of different clones of Cinnamomum camphora . Journal of Plant Resources and Environment 20(3), 5661.Google Scholar
Moncada, X. & Hinrichsen, P. (2007). Limited genetic diversity among clones of red wine cultivar ‘Carmenère’ as revealed by microsatellite and AFLP markers. Vitis: Journal of Grapevine Research 46, 174180.Google Scholar
Osman, F. N., Hashim, H., Al-Junid, S. A. M., Haron, M. A. B., Abdullah, N. E. & Muhammad, M. F. B. (2010). A statistical approach for rubber seed clones classification using reflectance index. In Proceedings of the IV Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation (AMS), 26–28 May 2010, Kota Kinabalu, Borneo (Eds Al-Dabass, D., Yunus, J., Ibrahim, Z., Sarbatly, R. & Abraham, A.), pp. 291295. Piscataway, NJ: IEEE.Google Scholar
Pande, P. (2011). Variation in wood properties and growth in some clones of Populus deltoides Bartr. ex Marsh. American Journal of Plant Sciences 2, 644649.Google Scholar
Peñuelas, J., Filella, I., Biel, C., Serrano, L. & Savé, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14, 18871905.Google Scholar
Regner, F., Wiedeck, E. & Stadlbauer, A. (2000). A differentiation and identification of white Riesling clones by genetic markers. Vitis: Journal of Grapevine Research 39, 103107.Google Scholar
Regner, F., Hack, R. & Santiago, J. L. (2006). Highly variable Vitis microsatellite loci for the identification of Pinot noir clones. Vitis: Journal of Grapevine Research 45, 8591.Google Scholar
Ribeiro Da Luz, B. (2006). Attenuated total reflectance spectroscopy of plant leaves: a tool for ecological and botanical studies. New Phytologist 172, 305318.Google Scholar
Sánchez-Azofeifa, G. A., Castro, K., Wright, S. J., Gamon, J., Kalacska, M., Rivard, B., Schnitzer, S. A. & Feng, J. L. (2009). Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments. Remote Sensing of Environment 113, 20762088.Google Scholar
Schläpfer, F. & Fischer, M. (1998). An isozyme study of clone diversity and relative importance of sexual and vegetative recruitment in the grass Brachypodium pinnatum . Ecography 21, 351360.Google Scholar
Silvestroni, O., Di Pietro, D., Intrieri, C., Vignani, R., Filippetti, I., Del Casino, C., Scali, M. & Cresti, M. (1997). Detection of genetic diversity among clones of cv. Fortana (Vitis vinifera L.) by microsatellite DNA polymorphism analysis. Vitis: Journal of Grapevine Research 36, 147150.Google Scholar
Slaton, M. R., Hunt, E. R. & Smith, W. K. (2001). Estimating near-infrared leaf reflectance from leaf structural characteristics. American Journal of Botany 88, 278284.Google Scholar
Taylor, S., Baker, D., Owuor, P., Orchard, J., Othieno, C. & Gay, C. (1992). A model for predicting black tea quality from the carotenoid and chlorophyll composition of fresh green tea leaf. Journal of the Science of Food and Agriculture 58, 185191.Google Scholar
Terashima, I. & Saeki, T. (1983). Light environment within a leaf I. Optical properties of paradermal sections of camellia leaves with special reference to differences in the optical properties of palisade and spongy tissues. Plant and Cell Physiology 24, 14931501.Google Scholar
Vogelmann, T. C. (1993). Plant-tissue optics. Annual Review of Plant Physiology and Plant Molecular Biology 44, 231251.Google Scholar
Wegscheider, E., Benjak, A. & Forneck, A. (2009). Clonal variation in pinot noir revealed by S-SAP involving universal retrotransposon-based sequences. American Journal of Enology and Viticulture 60, 104109.Google Scholar