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Digital Ampelographer: A CNN Based Preliminary Approach

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Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

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Abstract

Authenticity, traceability and certification are key to assure both quality and confidence to wine consumers and an added commercial value to farmers and winemakers. Grapevine variety stands out as one of the most relevant factors to be considered in wine identification within the whole wine sector value chain. Ampelography is the science responsible for grapevine varieties identification based on (i) in-situ visual inspection of grapevine mature leaves and (ii) on the ampelographer experience. Laboratorial analysis is a costly and time-consuming alternative. Both the lack of experienced professionals and context-induced error can severely hinder official regulatory authorities’ role and therefore bring about a significant impact in the value chain.

The purpose of this paper is to assess deep learning potential to classify grapevine varieties through the ampelometric analysis of leaves. Three convolutional neural networks architectures performance are evaluated using a dataset composed of six different grapevine varieties leaves. This preliminary approach identified Xception architecture as very promising to classify grapevine varieties and therefore support a future autonomous tool that assists the wine sector stakeholders, particularly the official regulatory authorities.

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Acknowledgements

The authors would like to acknowledge project “CHIC – Cooperative Holistic View on Internet and Content” (N° 24498), financed the European Regional Development Fund (ERDF) through COMPETE2020 - the Operational Programme for Competitiveness and Internationalisation (OPCI) that partially supported this work, Port and Douro Wines Institute, I. P. (IVDP, I.P.) for their collaboration in this work.

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Authors and Affiliations

  1. INESC Technology and Science (INESC-TEC), 4200-465, Porto, Portugal

    Telmo Adão, Tatiana M. Pinho, António Sousa, Joaquim J. Sousa, Emanuel Peres & Raul Morais

  2. University of Trás-os-Montes e Alto Douro, 5000-801, Vila Real, Portugal

    Telmo Adão, Tatiana M. Pinho, António Ferreira, António Sousa, Luís Pádua, José Sousa, Joaquim J. Sousa, Emanuel Peres & Raul Morais

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  1. Telmo Adão
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Contributions

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project «POCI-01-0145-FEDER-006961», and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.

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Correspondence to Telmo Adão .

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Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Adão, T. et al. (2019). Digital Ampelographer: A CNN Based Preliminary Approach. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_23

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  • DOI: https://doi.org/10.1007/978-3-030-30241-2_23

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