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- Title
Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries.
- Authors
Gomes, Véronique; Mendes-Ferreira, Ana; Melo-Pinto, Pedro
- Abstract
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.
- Subjects
GRAPES; DEEP learning; ARTIFICIAL neural networks; CONVOLUTIONAL neural networks; BERRIES; MACHINE learning
- Publication
Sensors (14248220), 2021, Vol 21, Issue 10, p3459
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21103459