Data mining and NIR spectroscopy in viticulture: Applications for plant phenotyping under field conditions

Autor: Gutiérrez S.; Tardáguila Laso, Javier; Fernández-Novales J.; Diago Santamaría, María Paz

Tipo de documento: Artículo de revista

Revista: Sensors. ISSN: 1424-8220. Año: 2016. Número: 2. Volumen: 16.

Texto completo open access 

CIRC: GRUPO A - EXCELENCIA

Referencias:

  • Walter, A., Liebisch, F., Hund, A., Plant phenotyping: From bean weighing to image analysis (2015) Plant Methods, p. 11
  • Minervini, M., Scharr, H., Tsaftaris, S., Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner] (2015) IEEE Signal Process. Mag, 32, pp. 126-131
  • Fiorani, F., Schurr, U., Future scenarios for plant phenotyping (2013) Annu. Rev. Plant Biol, 64, pp. 267-291
  • Roberts, C.A., Workman, J., Reeves, J.B., (2004) Near-Infrared Spectroscopy in Agriculture, , American Society of Agronomy: Madison, WI, USA
  • Cozzolino, D., Esler, M., Dambergs, R., Cynkar, W., Boehm, D., Francis, I., Gishen, M., Prediction of colour and pH in grapes using a diode array spectrophotometer (400-1100 nm) (2004) J. near Infrared Spectrosc, 12, pp. 105-112
  • Smith, J., Schmidtke, L., Müller, M., Holzapfel, B., Measurement of the concentration of nutrients in grapevine petioles by attenuated total reflectance Fourier transform infrared spectroscopy and chemometrics (2014) Aust. J. Grape Wine Res., 20, pp. 299-309
  • Rustioni, L., Rocchi, L., Guffanti, E., Cola, G., Failla, O., Characterization of grape (Vitis vinifera L.) berry sunburn symptoms by reflectance (2014) J. Agric. Food Chem, 62, pp. 3043-3046
  • Han, J., Kamber, M., Pei, J., (2011) Data Mining: Concepts and Techniques: Concepts and Techniques, , Elsevier: Harrisburg, PA, USA
  • Witten, I.H., Frank, E., (2005) Data Mining: Practical Machine Learning Tools and Techniques, , Morgan Kaufmann: Burlington, VT, USA
  • Quinlan, J.R., Induction of decision trees (1986) Mach. Learn, 1, pp. 81-106
  • Rokach, L., Decision forest: Twenty years of research (2016) Inf. Fusion, 27, pp. 111-125
  • Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors (1986) Nature
  • Cortes, C., Vapnik, V., Support-vector networks (1995) Mach. Learn, 20, pp. 273-297
  • Lavrač, N., Selected techniques for data mining in medicine (1999) Artif. Intell. Med, 16, pp. 3-23
  • Giudici, P., (2005) Applied Data Mining: Statistical Methods for Business and Industry, , John Wiley &
  • Sons: New York, NY, USA
  • Hirschman, L., Park, J.C., Tsujii, J., Wong, L., Wu, C.H., Accomplishments and challenges in literature data mining for biology (2002) Bioinformatics, 18, pp. 1553-1561
  • Boser, B.E., Guyon, I.M., Vapnik, V., Training algorithm for optimal margin classifiers (1992) Proceedings of the Fifth Annual Workshop on Computational Learning Theory. Pittsburgh, PA, USA, pp. 144-152. , 27-29 July
  • Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J., Rotation forest: A new classifier ensemble method (2006) IEEE Trans. Pattern Anal. Mach. Intell, 28, pp. 1619-1630
  • Quinlan, J.R., Learning with continuous classes (1992) Proceedings of the 5Th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, pp. 343-348. , 16-18November
  • Wold, S., Sjöström, M., Eriksson, L., PLS-regression: A basic tool of chemometrics (2001) Chemom. Intell. Lab. Syst, 58, pp. 109-130
  • Kisi, O., Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree (2015) J. Hydrol, 528, pp. 312-320
  • Štravs, L., Brilly, M., Development of a low-flow forecasting model using the M5 machine learning method (2007) Hydrol. Sci. J, 52, pp. 466-477
  • Bhattacharya, B., Solomatine, D.P., Neural networks and M5 model trees in modeling water level-discharge relationship (2005) Neurocomputing, 63, pp. 381-396
  • Diago, M., Fernandes, A., Millan, B., Tardaguila, J., Melo-Pinto, P., Identification of grapevine varieties using leaf spectroscopy and partial least squares (2013) Comput. Electron. Agric, 99, pp. 7-13
  • Jones, H.G., Irrigation scheduling: Advantages and pitfalls of plant-based methods (2004) J. Exp. Bot, 55, pp. 2427-2436
  • De Bei, R., Cozzolino, D., Sullivan, W., Cynkar, W., Fuentes, S., Dambergs, R., Pech, J., Tyerman, S., Non-destructive measurement of grapevine water potential using near infrared spectroscopy (2011) Aust. J. Grape Wine Res, 17, pp. 62-71
  • Poblete-Echeverría, C., Ortega-Farías, S., Lobos, G., Romero, S., Ahumada, L., Escobar, A., Fuentes, S., Non-invasive method to monitor plant water potential of an olive orchard using visible and near infrared spectroscopy analysis (2014) Acta Hortic, 1057, pp. 363-368
  • Vila, H., Hugalde, I., Filippo, D., M. Estimation of leaf water potential by thermographic and spectral measurements in grapevine (2011) RIA Rev. De Investig. Agropecu, 37, pp. 46-53
  • Santos, A.O., Kaye, O., Grapevine leaf water potential based upon near infrared spectroscopy (2009) Sci. Agric, 66, pp. 287-292
  • Perez, D., Sanchez, M., Cano, G., Garrido, A., Authentication of Green Asparagus Varieties by Near-Infrared Reflectance Spectroscopy (2001) J. Food Sci, 66, pp. 323-327
  • Naes, T., Isaksson, T., Fearn, T., Davies, T., (2002) A User Friendly Guide to Multivariate Calibration and Classification, , NIR publications: Chichester, UK
  • Scholander, P.F., Bradstreet, E.D., Hemmingsen, E., Hammel, H., Sap pressure in vascular plants negative hydrostatic pressure can be measured in plants (1965) Science, 148, pp. 339-346
  • Rinnan, Å., Van Den Berg, F., Engelsen, S.B., Review of the most common pre-processing techniques for near-infrared spectra (2009) Trac Trends Anal. Chem, 28, pp. 1201-1222
  • Barnes, R., Dhanoa, M., Lister, S., Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra (1989) Appl. Spectrosc, 43, pp. 772-777
  • Dhanoa, M., Lister, S., Barnes, R., On the scales associated with near-infrared reflectance difference spectra (1995) Appl. Spectrosc, 49, pp. 765-772
  • Savitzky, A., Golay, M., Smoothing and differentiation of data by simplified least squares procedures (1964) Anal. Chem, 36, pp. 1627-1639
  • Williamson, R., Bartlett, P., New support vector algorithms (2000) Neural Comput, 12, pp. 1207-1245
  • Chang, C.C., Lin, C.J., LIBSVM: A library for support vector machines (2011) ACM Trans. Intell. Syst. Technol, p. 2
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software: An update (2009) ACM SIGKDD Explor. Newsl, 11, pp. 10-18
  • Galet, P., (1979) A Practical Ampelography, , Cornell University Press: New York, NY, USA
  • Pelsy, F., Hocquigny, S., Moncada, X., Barbeau, G., Forget, D., Hinrichsen, P., Merdinoglu, D., An extensive study of the genetic diversity within seven French wine grape variety collections (2010) Theor. Appl. Genet, 120, pp. 1219-1231
  • Altube, H., Cabello, F., Ortiz, J., Caracterización de variedades y portainjertos de vid mediante isoenzimas de los sarmientos (1991) Vitis, 30, pp. 203-212
  • Gutiérrez, S., Tardaguila, J., Fernández-Novales, J., Diago, M.P., Support vector machine and artificial neural network models for the classification of grapevine varieties using a portable NIR spectrophotometer (2015) PLOS ONE, p. 10
  • Yang, X., Hong, H., You, Z., Cheng, F., Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification (2015) Sensors, 15, pp. 15578-15594
  • Sánchez, M.T., De La Haba, M.J., Benítez-López, M., Fernández-Novales, J., Garrido-Varo, A., Pérez-Marín, D., Non-destructive characterization and quality control of intact strawberries based on NIR spectral data (2012) J. Food Eng, 110, pp. 102-108
  • Pérez-Marín, D., Paz, P., Guerrero, J.E., Garrido-Varo, A., Sánchez, M.T., Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums (2010) J. Food Eng, 99, pp. 294-302
  • Shenk, J.S., Workman, J.J., Westerhaus, M.O., Application of NIR spectroscopy to agricultural products (2001) Pract. Spectrosc. Ser, 27, pp. 419-474
  • Diago, M., Pou, A., Millan, B., Tardaguila, J., Fernandes, A., Melo-Pinto, P., Assessment of grapevine water status from hyperspectral imaging of leaves (2014) Acta Hortic, 1038, pp. 89-96
  • Brereton, R.G., (2007) Applied Chemometrics for Scientists, , John Wiley &
  • Sons: New York, NY, USA
  • Wong, T.T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation (2015) Pattern Recognit, 48, pp. 2839-2846