Synergetic application of E-tongue and E-eye based on deep learning to discrimination of Pu-erh tea storage time

• Published in: Computers and electronics in agriculture Vol. 187; p. 106297
• Main Authors: Yang, Zhengwei, Gao, Jiyong, Wang, Shoucheng, Wang, Zhiqiang, Li, Caihong, Lan, Yubin, Sun, Xia, Li, Shengxi
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: Algorithms; Artificial neural networks; Back propagation; Back propagation networks; Data fusion; Data integration; Deep learning; Electronic eye; Electronic tongue; Electronic tongues; Feature extraction; Machine learning; Neural networks; Optimization; Pu-erh tea; Deep learning; Data fusion; Electronic tongue; Electronic eye; Pu-erh tea
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106297

[Display omitted] •Electronic tongue and eye are used to classify Pu-erh tea via feature-level fusion.•The deep learning is introduced to data fusion field of intelligent sensory system.•CNN structures are designed and optimized by Bayesian optimization.•The proposed model has better performance compared with conventional methods. This study proposed an efficient approach that an electronic tongue (ET) and an electronic eye (EE) combined with a deep learning algorithm were jointly leveraged to recognition Pu-erh tea. A one-dimensional convolutional neural network (1-D CNN) and a two-dimensional convolutional neural network (2-D CNN) were designed and optimized for the feature extraction of ET and EE signals, respectively. Then, a feature-level fusion strategy was introduced to address the feature vectors extracted from the different types of CNN models. To highlight the effect of data fusion, a backpropagation neural network (BPNN), a classifier similar to the fully connected layers of CNN models, was employed. Meanwhile, the Bayesian optimization algorithm (BOA) was employed for hyperparameter optimization of the identification models. The experimental results showed that the feature fusion strategy assimilated the merits of the ET and EE and gained better Pu-erh tea identification performance than an independent intelligent sensory system combined with CNN model. The results demonstrate that the feature-level fusion based on deep learning algorithm gained the best accuracy on the test set, with a precision, a recall, an F1-score and an AUC score of 99.07%, 99.2%, 0.992 and 0.994, respectively. This study shows that the simultaneous utilization of an ET and an EE combined with deep learning algorithm could function as a rapid detection method for discriminating the storage time of Pu-erh tea.