Abstract
Grape varieties play an important role in wine’s production chain, its identification is crucial for controlling and regulating the production. Nowadays, two techniques are widely used, ampelography and molecular analysis. However, there are problems with both of them. In this scenario, Deep Learning classifiers emerged as a tool to automatically classify grape varieties. A problem with the classification of on-field acquired images is that there is a lot of information unrelated to the target classification. In this study, the use of segmentation before classification to remove such unrelated information was analyzed. We used two grape varieties identification datasets to fine-tune a pre-trained EfficientNetV2S. Our results showed that segmentation can slightly improve classification performance if only unrelated information is removed.
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Acknowledgments
This work was supported by the project “DATI-Digital Agriculture Technologies for Irrigation efficiency”, PRIMA-Partnership for Research and Innovation in the Mediterranean Area, (Research and Innovation activities), financed by the states participating in the PRIMA partnership and by the European Union through Horizon 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.
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Carneiro, G.A., Texeira, A., Morais, R., Sousa, J.J., Cunha, A. (2023). Can the Segmentation Improve the Grape Varieties’ Identification Through Images Acquired On-Field?. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_28
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