Evaluation of Table Grape Flavor Based on Deep Neural Networks - Zheng Liu; Yu Zhang; Yicheng Zhang; Lei Guo; Chase Wu; Wei Shen (Applied Sciences, 2023)
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.