Non-invasive setup for grape maturation classification using deep learning.

Background: The San Francisco Valley region from Brazil is known worldwide for its fruit production and exportation, especially grapes and wines. The grapes have high quality not only due to the excellent morphological characteristics, but also to the pleasant taste of their fruits. Such features are obtained because of the climatic conditions present in the region. In addition to the favorable climate for grape cultivation, harvesting at the right time interferes with fruit properties.

Results: This work aims to define grape maturation stage of Syrah and Cabernet Sauvignon cultivars with the aid of deep-learning models. The idea of working with these algorithms came from the fact that the techniques commonly used to find the ideal harvesting point are invasive, expensive, and take a long time to get their results. In this work, convolutional neural networks were used in an image classification system, in which grape images were acquired, preprocessed, and classified based on their maturation stage. Images were acquired with varying illuminants that were considered as parameters of the classification models, as well as the different post-harvesting weeks. The best models achieved maturation classification accuracy of 93.41% and 72.66% for Syrah and Cabernet Sauvignon respectively.

Conclusions: It was possible to correctly classify wine grapes using computational intelligent algorithms with high accuracy, regarding the harvesting time, corroborating chemometric results. © 2020 Society of Chemical Industry.