Automated grapevine cultivar classification based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy parameters

The application of computer vision algorithms and chemometric fingerprinting using near-infrared spectrometry (NIR) of plant leaves, offers enhanced capabilities for ampelography by providing more accurate methods to discriminate leaves based on morphological parameters, and chemometrics, respectively. This paper showed that machine learning algorithms based on morpho-colorimetric parameters and NIR analysis separately, were able to automatically classify leaves of 16 grapevine cultivars. The artificial neural network (ANN) model developed with morpho-colorimetric parameters as inputs (Model 1), and 16 cultivars as targets, rendered an accuracy of 94% to classify leaves for all cultivars studied. The ANN model obtained with the NIR spectra per leaf as inputs (Model 2), and the real classification as targets, rendered 92% accuracy. The automatic extraction of morpho-colorimetric data, NIR chemical fingerprinting and machine learning modelling rendered rapid, accurate and non-destructive methods for cultivar classification, which can aid management practices.