Freshness recognition and remaining shelf life prediction of banana based on attention Temporal Convolutional Network - LI Xin; ZHU Lei; ZHANG Yuan; DU Yanping; XING Xiao (Shipin yu jixie)
ObjectiveTo 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.MethodsTaking 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.ResultsThe recognition accuracies for BP, SVM, TCN, and ECA-TCN were 84.89%, 85.16%, 97.83%, and 99.03%, respectively.ConclusionThe experimental method demonstrates superior performance in recognizing the freshness of bananas.