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Article

Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries

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CITAB—Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Inov4Agro—Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
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WM&B—Laboratory of Wine Microbiology & Biotechnology, Department of Biology and Environment, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
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BioISI—Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal
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Departamento de Engenharias, Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
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Author to whom correspondence should be addressed.
Sensors 2021, 21(10), 3459; https://doi.org/10.3390/s21103459
Received: 1 April 2021 / Revised: 19 April 2021 / Accepted: 11 May 2021 / Published: 15 May 2021
(This article belongs to the Section Intelligent Sensors)
Remote sensing technology, such as hyperspectral imaging, in combination with machine learning algorithms, has emerged as a viable tool for rapid and nondestructive assessment of wine grape ripeness. However, the differences in terroir, together with the climatic variations and the variability exhibited by different grape varieties, have a considerable impact on the grape ripening stages within a vintage and between vintages and, consequently, on the robustness of the predictive models. To address this challenge, we present a novel one-dimensional convolutional neural network architecture-based model for the prediction of sugar content and pH, using reflectance hyperspectral data from different vintages. We aimed to evaluate the model’s generalization capacity for different varieties and for a different vintage not employed in the training process, using independent test sets. A transfer learning mechanism, based on the proposed convolutional neural network, was also used to evaluate improvements in the model’s generalization. Overall, the results for generalization ability showed a very good performance with RMSEP values of 1.118 °Brix and 1.085 °Brix for sugar content and 0.199 and 0.183 for pH, for test sets using different varieties and a different vintage, respectively, improving and updating the current state of the art. View Full-Text
Keywords: machine learning; convolutional neural networks; transfer learning; hyperspectral imaging; prediction; grape berries machine learning; convolutional neural networks; transfer learning; hyperspectral imaging; prediction; grape berries
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MDPI and ACS Style

Gomes, V.; Mendes-Ferreira, A.; Melo-Pinto, P. Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries. Sensors 2021, 21, 3459. https://doi.org/10.3390/s21103459

AMA Style

Gomes V, Mendes-Ferreira A, Melo-Pinto P. Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries. Sensors. 2021; 21(10):3459. https://doi.org/10.3390/s21103459

Chicago/Turabian Style

Gomes, Véronique, Ana Mendes-Ferreira, and Pedro Melo-Pinto. 2021. "Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries" Sensors 21, no. 10: 3459. https://doi.org/10.3390/s21103459

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