Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions†
Instituto de Ciencias de la Vid y del Vino (University of La Rioja, CSIC, Gobierno de La Rioja) Ctra. De Burgos Km, 6, 26007 Logroño, Spain
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Author to whom correspondence should be addressed.
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Proceedings of the 2nd International Electronic Conference on Sensors and Applications, online, 15–30 November 2015.
Academic Editor: Dirk Lehmhus
Sensors 2016, 16(2), 236; https://doi.org/10.3390/s16020236
Received: 2 December 2015 / Revised: 29 January 2016 / Accepted: 4 February 2016 / Published: 16 February 2016
(This article belongs to the Special Issue Selected Papers from the 2nd International Electronic Conference on Sensors and Applications)
Plant phenotyping is a very important topic in agriculture. In this context, data mining strategies may be applied to agricultural data retrieved with new non-invasive devices, with the aim of yielding useful, reliable and objective information. This work presents some applications of machine learning algorithms along with in-field acquired NIR spectral data for plant phenotyping in viticulture, specifically for grapevine variety discrimination and assessment of plant water status. Support vector machine (SVM), rotation forests and M5 trees models were built using NIR spectra acquired in the field directly on the adaxial side of grapevine leaves, with a non-invasive portable spectrophotometer working in the spectral range between 1600 and 2400 nm. The ν-SVM algorithm was used for the training of a model for varietal classification. The classifiers’ performance for the 10 varieties reached, for cross- and external validations, the 88.7% and 92.5% marks, respectively. For water stress assessment, the models developed using the absorbance spectra of six varieties yielded the same determination coefficient for both cross- and external validations (R2 = 0.84; RMSEs of 0.164 and 0.165 MPa, respectively). Furthermore, a variety-specific model trained only with samples of Tempranillo from two different vintages yielded R2 = 0.76 and RMSE of 0.16 MPa for cross-validation and R2 = 0.79, RMSE of 0.17 MPa for external validation. These results show the power of the combined use of data mining and non-invasive NIR sensing for in-field grapevine phenotyping and their usefulness for the wine industry and precision viticulture implementations.
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Keywords:
variety classification; plant water status; non-destructive; SVM; rotation forest; regression tree; stem water potential
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MDPI and ACS Style
Gutiérrez, S.; Tardaguila, J.; Fernández-Novales, J.; Diago, M.P. Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions. Sensors 2016, 16, 236. https://doi.org/10.3390/s16020236
AMA Style
Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP. Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions. Sensors. 2016; 16(2):236. https://doi.org/10.3390/s16020236
Chicago/Turabian StyleGutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, Maria P. 2016. "Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions" Sensors 16, no. 2: 236. https://doi.org/10.3390/s16020236
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