Blueberry Remaining Shelf-Life Prediction Based on the PSO-CNN-BiLSTM-MHA Model

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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Bibliographic Details
Main Authors: Mengya Liu, Xu Cheng, Yu Cao, Qian Zhou
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Bidirectional long short-term memory network
convolutional neural network
multi-head attention mechanisms
particle swarm optimization
remaining shelf-life prediction
Online Access:https://ieeexplore.ieee.org/document/10915596/
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https://ieeexplore.ieee.org/document/10915596/

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