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Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model

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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)

Abstract

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.

Suggested Citation

  • Teodora Basile & Antonio Maria Amendolagine & Luigi Tarricone, 2022. "Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model," Agriculture, MDPI, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:5-:d:1009020
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    References listed on IDEAS

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    1. Jie Yang & Bin Yue & Feifei Feng & Jinfa Shi & Haoyang Zong & Junxu Ma & Linjian Shangguan & Shuai Li & Junwei Ma, 2022. "Concrete Vehicle Scheduling Based on Immune Genetic Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-15, June.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
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    Keywords

    NIR; genetic algorithm; LDA; PCA; ANN; grape;
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