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Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks

2019; Elsevier BV; Volume: 163; Linguagem: Inglês

10.1016/j.compag.2019.104855

1872-7107

Armando Fernandes, Andrei B. Utkin, José Eiras‐Dias, Jorge Cunha, José Silvestre, Pedro Melo‐Pinto,

Fermentation and Sensory Analysis

Several experiments have been previously reported suggesting that the application of spectroscopy and machine learning allows the identification of grapevine varieties, however, up to now, the maximum number of varieties separated was twenty and the total number of sample spectra used does not go beyond the few hundreds. The present work aim is to answer the question: Is it possible to separate one variety from an enlarged group of other varieties when the number of samples is also significantly increased? With this in mind, a total of 35,833 spectra from leaves of 626 plants from 64 varieties were gathered for the study. This is a non-trivial evolution from previous works because it originates an increase in the variability of spectra which brings in a higher risk that a significant percentage of spectra of different varieties are equal and cannot be separated. Simultaneously, it was studied if a miniaturized and easy to use spectrometer could deliver data whose quality was enough to allow varieties separation even with data being collected in the field, non-destructively, and under uncontrolled solar lighting. This data was used to build support vector machines and convolutional neural networks for separating Touriga Nacional from 63 other varieties (including Touriga Franca) or Touriga Franca from 63 varieties (including Touriga Nacional), and the classification efficiencies are analysed.