An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network

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dc.authoridAlhudhaif, Adi/0000-0002-7201-6963
dc.contributor.authorYuan, Yue
dc.contributor.authorChen, Jichi
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.date.accessioned2024-09-25T19:58:35Z
dc.date.available2024-09-25T19:58:35Z
dc.date.issued2024
dc.departmentAbant İzzet Baysal Üniversitesien_US
dc.description.abstractFruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combined with machine learning. However, manual extraction of features has the problem of poor adaptability, resulting in low efficiency in fruit and vegetable freshness detection. Although exist some studies that have introduced deep learning methods to automatically learn deep features that characterize the freshness of fruits and vegetables to cope with the diversity and variability in complex scenes. However, the performance of these studies on fruit and vegetable freshness detection needs to be further improved. Based on this, this paper proposes a novel method that fusion of different deep learning models to extract the features of fruit and vegetable images and the correlation between various areas in the image, so as to detect the freshness of fruits and vegetables more objectively and accurately. First, the image size in the dataset is resized to meet the input requirements of the deep learning model. Then, deep features characterizing the freshness of fruits and vegetables are extracted by the fused deep learning model. Finally, the parameters of the fusion model were optimized based on the detection performance of the fused deep learning model, and the performance of fruit and vegetable freshness detection was evaluated. Experimental results show that the CNN_BiLSTM deep learning model, which fusion convolutional neural network (CNN) and bidirectional long -short term memory neural network (BiLSTM), is combined with parameter optimization processing to achieve an accuracy of 97.76% in detecting the freshness of fruits and vegetables. The research results show that this method is promising to improve the performance of fruit and vegetable freshness detection.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62101355]en_US
dc.description.sponsorshipThis work was supported by National Natural Science Foundation of China (62101355) .en_US
dc.identifier.doi10.1016/j.crfs.2024.100723
dc.identifier.issn2665-9271
dc.identifier.pmid39022740en_US
dc.identifier.scopus2-s2.0-85188786829en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.crfs.2024.100723
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13627
dc.identifier.volume8en_US
dc.identifier.wosWOS:001219646700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofCurrent Research in Food Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzYK_20240925en_US
dc.subjectFruit and vegetable freshness detectionen_US
dc.subjectCNNen_US
dc.subjectBiLSTMen_US
dc.subjectModel fusionen_US
dc.subjectParameter optimizationen_US
dc.titleAn innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory networken_US
dc.typeArticleen_US

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