Evaluation of Table Grape Flavor Based on Deep Neural Networks

Zheng Liu, Yu Zhang, Yicheng Zhang, Lei Guo, Chase Wu, Wei Shen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


For fresh table grapes, flavor is one of the most important components of their overall quality. The flavor of table grapes includes both their taste and aroma, involving multiple physical and chemical properties, such as soluble solids. In this paper, we investigate six factors, divide flavor ratings into a range of five grades based on the results of trained food tasters, and propose a deep-neural-network-based flavor evaluation model that integrates an attention mechanism. After training, the proposed model achieved a prediction accuracy of 94.8% with an average difference of 2.657 points between the predicted score and the actual score. This work provides a promising solution to the evaluation of table grapes and has the potential to improve product quality for future breeding in agricultural engineering.

Original languageEnglish (US)
Article number6532
JournalApplied Sciences (Switzerland)
Issue number11
StatePublished - Jun 2023

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


  • attention mechanism
  • deep neural networks
  • evaluation model
  • grape flavor evaluation


Dive into the research topics of 'Evaluation of Table Grape Flavor Based on Deep Neural Networks'. Together they form a unique fingerprint.

Cite this