Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model
Teodora Basile (),
Antonio Maria Amendolagine and
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Teodora Basile: Consiglio per la Ricerca in Agricoltura e L’analisi dell’Economia Agraria—Centro di Ricerca Viticoltura ed Enologia (CREA—VE), 70010 Turi, BA, Italy
Antonio Maria Amendolagine: Consiglio per la Ricerca in Agricoltura e L’analisi dell’Economia Agraria—Centro di Ricerca Viticoltura ed Enologia (CREA—VE), 70010 Turi, BA, Italy
Luigi Tarricone: Consiglio per la Ricerca in Agricoltura e L’analisi dell’Economia Agraria—Centro di Ricerca Viticoltura ed Enologia (CREA—VE), 70010 Turi, BA, Italy
Agriculture, 2022, vol. 13, issue 1, 1-11
In this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO FT-NIR spectrometer (Bruker, Germany) with 8 cm −1 resolution and 64 scans in the wave number range of 4000–10,000 cm −1 . The chemometric analyses were performed with the statistical software R version 4.2.0 (2022-04-22). Elimination of uninformative variables was accomplished with a PCA and a genetic algorithm (GA). The discrimination performance of a linear discriminant analysis (LDA) model was not enhanced with either a PCA- or a GA-based selection. A multiclass classification model was built with an artificial neural network (ANN). The best fit multiclass classification model on test data was obtained with the GA-ANN model that gave a classification accuracy of close to 80% for samples belonging to the four classes. These results demonstrate that NIR spectroscopy could be used as a rapid method for the classification of berries based on their rootstock x cover-crops combination.
Keywords: NIR; genetic algorithm; LDA; PCA; ANN; grape (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2022:i:1:p:5-:d:1009020
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