Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model
Remaining shelf-life prediction of blueberries is crucial in reducing economic losses and enhancing market competitiveness. To meet the demand for high accuracy in predicting the remaining shelf-life of blueberries, this paper proposes a fusion model (PSO-CNN-BiLSTM-MHA) that combines Particle Swarm...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10915596/ |
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| author | Mengya Liu Xu Cheng Yu Cao Qian Zhou |
| author_facet | Mengya Liu Xu Cheng Yu Cao Qian Zhou |
| author_sort | Mengya Liu |
| collection | DOAJ |
| description | Remaining shelf-life prediction of blueberries is crucial in reducing economic losses and enhancing market competitiveness. To meet the demand for high accuracy in predicting the remaining shelf-life of blueberries, this paper proposes a fusion model (PSO-CNN-BiLSTM-MHA) that combines Particle Swarm Optimization (PSO), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM), and Multi-Head Attention (MHA) mechanisms for predicting the remaining shelf-life of ‘Emerald’ blueberries under two temperature conditions, <inline-formula> <tex-math notation="LaTeX">$4^{\circ } \text {C}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$25^{\circ } \text {C}$ </tex-math></inline-formula>. In this study, seven key features were selected from fifteen quality indicators of blueberries using the embedded method, and the PSO algorithm was used to determine the optimal hyperparameter combination of the model, which effectively improved its prediction performance. The experimental results show that our model outperforms other models in all evaluation metrics under both temperature conditions and demonstrates excellent prediction ability and stability. This study provides an effective technical approach for the accurate prediction of the remaining shelf-life of blueberries and other fruits. |
| format | Article |
| id | doaj-art-15c150e1136646a3bc624f51f987d478 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-15c150e1136646a3bc624f51f987d4782025-08-20T02:56:39ZengIEEEIEEE Access2169-35362025-01-0113432384324810.1109/ACCESS.2025.354872010915596Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA ModelMengya Liu0https://orcid.org/0009-0008-7268-5834Xu Cheng1https://orcid.org/0000-0002-9559-7721Yu Cao2https://orcid.org/0009-0006-7562-5680Qian Zhou3School of Information Science and Control Engineering, Liaoning Petrochemical University, Fushun, ChinaCollege of Economics and Management, Shenyang Agricultural University, Shenyang, ChinaSchool of Information Science and Control Engineering, Liaoning Petrochemical University, Fushun, ChinaFood Science College, Shenyang Agricultural University, Shenyang, ChinaRemaining shelf-life prediction of blueberries is crucial in reducing economic losses and enhancing market competitiveness. To meet the demand for high accuracy in predicting the remaining shelf-life of blueberries, this paper proposes a fusion model (PSO-CNN-BiLSTM-MHA) that combines Particle Swarm Optimization (PSO), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM), and Multi-Head Attention (MHA) mechanisms for predicting the remaining shelf-life of ‘Emerald’ blueberries under two temperature conditions, <inline-formula> <tex-math notation="LaTeX">$4^{\circ } \text {C}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$25^{\circ } \text {C}$ </tex-math></inline-formula>. In this study, seven key features were selected from fifteen quality indicators of blueberries using the embedded method, and the PSO algorithm was used to determine the optimal hyperparameter combination of the model, which effectively improved its prediction performance. The experimental results show that our model outperforms other models in all evaluation metrics under both temperature conditions and demonstrates excellent prediction ability and stability. This study provides an effective technical approach for the accurate prediction of the remaining shelf-life of blueberries and other fruits.https://ieeexplore.ieee.org/document/10915596/Bidirectional long short-term memory networkconvolutional neural networkmulti-head attention mechanismsparticle swarm optimizationremaining shelf-life prediction |
| spellingShingle | Mengya Liu Xu Cheng Yu Cao Qian Zhou Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model IEEE Access Bidirectional long short-term memory network convolutional neural network multi-head attention mechanisms particle swarm optimization remaining shelf-life prediction |
| title | Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model |
| title_full | Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model |
| title_fullStr | Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model |
| title_full_unstemmed | Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model |
| title_short | Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model |
| title_sort | blueberry remaining shelf life prediction based on the pso cnn bilstm mha model |
| topic | Bidirectional long short-term memory network convolutional neural network multi-head attention mechanisms particle swarm optimization remaining shelf-life prediction |
| url | https://ieeexplore.ieee.org/document/10915596/ |
| work_keys_str_mv | AT mengyaliu blueberryremainingshelflifepredictionbasedonthepsocnnbilstmmhamodel AT xucheng blueberryremainingshelflifepredictionbasedonthepsocnnbilstmmhamodel AT yucao blueberryremainingshelflifepredictionbasedonthepsocnnbilstmmhamodel AT qianzhou blueberryremainingshelflifepredictionbasedonthepsocnnbilstmmhamodel |