Proximal hyperspectral analysis in grape leaves for region and variety identification

Abstract

ABSTRACT: Reflectance measurements of plants of the same species can produce sets of data with differences between spectra, due to factors that can be external to the plant, like the environment where the plant grows, and to internal factors, for measurements of different varieties. This paper reports results of the analysis of radiometric measurements performed on leaves of vines of several grape varieties and on several sites. The objective of the research was, after the application of techniques of dimensionality reduction for the definition of the most relevant wavelengths, to evaluate four machine learning models applied to the observational sample aiming to discriminate classes of region and variety in vineyards. The tested machine learning classification models were Canonical Discrimination Analysis (CDA), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). From the results, we reported that the LGBM model obtained better accuracy in spectral discrimination by region, with a value the 0.93, followed by the RF model. Regarding the discrimination between grape varieties, these two models also achieved better results, with accuracies of 0.88 and 0.89. The wavelengths more relevant for discrimination were at ultraviolet, followed by those at blue and green spectral regions. This research pointed toward the importance of defining the wavelengths more relevant to the characterization of the reflectance spectra of leaves of grape varieties and revealed the effective capability of discriminating vineyards by their region or grape variety, using machine learning models.