Abstract
［Objective］ To address the issue of traditional machine learning algorithms (BP,SVM ) struggling to effectively extract features from time series data,which leads to subpar model recognition and prediction performance,and aim to minimize the freshness loss of fresh fruits during the distribution process.［Methods］ Taking bananas as the research subject,established a banana freshness recognition model (ECA -TCN ) by combining Time Convolutional Networks (TCN ) with Efficient Channel Attention Networks (ECA -NET ) and conduct simulation tests.［Results］］ The recognition accuracies for BP,SVM,TCN,and ECA -TCN were 84.89%,85.16%,97.83%,and 99.03%,respectively.［Conclusion］ The experimental method demonstrates superior performance in recognizing the freshness of bananas.
Publication Date
2-18-2025
First Page
153
Last Page
159
DOI
10.13652/j.spjx.1003.5788.2024.80299
Recommended Citation
Xin, LI; Lei, ZHU; Yuan, ZHANG; Yanping, DU; and Xiao, XING
(2025)
"Freshness recognition and remaining shelf life prediction of banana based on attention Temporal Convolutional Network,"
Food and Machinery: Vol. 40:
Iss.
11, Article 21.
DOI: 10.13652/j.spjx.1003.5788.2024.80299
Available at:
https://www.ifoodmm.cn/journal/vol40/iss11/21
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