Boosting the performance of SOTA convolution-based networks with dimensionality reduction: An application on hyperspectral images of wine grape berries

Abstract

Precision viticulture is an area that is very dependent on methods that allow for a sustainable assessment of grape maturity and, in this work, we apply two state-of-the-art (SOTA) convolution-based networks, namely InceptionTime and OmniScale 1D-CNN, to hyperspectral images of wine grape berries to estimate sugar content. Since attaining generalization capacity and processing the information in such high-dimensional data are the two biggest challenges to overcome in problems of this nature, we also study the impact of two dimensionality reduction techniques, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), on the models' performance. Both models underwent different tests with different vintages and varieties of wine grapes in the training/validation steps, as to form a true test to their generalization capacity. Our results show that both PCA and t-SNE succeed in improving the performance of these deep networks when an adequate number of components is chosen that minimizes the ratio between information loss and removing redundant features: additionally, both techniques significantly reduce computational cost, a very important trait when training deep learning models. Both models showed good generalization ability with very competitive results across different varieties and vintages even despite their significant differences in variability, which is an indicator that a relationship between spectras can be found that is reflected on sugar content values.

Keywords

• Hyperspectral images
• Wine grape berries
• Oenological parameters
• InceptionTime
• OmniScale 1D-CNN
• Dimensionality reduction