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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
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Non-invasive setup for grape maturation classification using deep learning.

Rodrigo P Ramos1, Jéssica S Gomes1, Ricardo M Prates1

  • 1College of Electrical Engineering, Federal University of São Francisco Valley (Univasf), Juazeiro, Brazil.

Journal of the Science of Food and Agriculture
|September 19, 2020
PubMed
Summary

Deep learning models accurately classify wine grape maturation stages for optimal harvesting. This automated approach surpasses traditional invasive methods, ensuring high-quality grape and wine production.

Keywords:
deep learninggrape maturationimage processingpost-harvest

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Viticulture

Background:

  • The San Francisco Valley region in Brazil is globally recognized for high-quality fruit production, particularly grapes and wines.
  • Grape quality is influenced by regional climate, morphological characteristics, and crucially, harvesting time.

Purpose of the Study:

  • To develop deep learning models for classifying the maturation stage of Syrah and Cabernet Sauvignon grape cultivars.
  • To provide an efficient and non-invasive alternative to traditional grape harvesting time determination methods.

Main Methods:

  • Utilized convolutional neural networks (CNNs) for image classification of grape maturation.
  • Trained models using preprocessed images of grapes under varying illuminants and post-harvesting weeks.

Main Results:

  • Achieved high accuracy in classifying grape maturation stages: 93.41% for Syrah and 72.66% for Cabernet Sauvignon.
  • Demonstrated the effectiveness of deep learning in assessing grape ripeness.

Conclusions:

  • Computational intelligence algorithms can accurately classify wine grape maturation for optimal harvesting.
  • The findings align with chemometric results, validating the approach for wine production.