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ACDM 2024 Digital Media Special Issue
Volume: 69 | Article ID: 010402
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Banana Shelf Life Prediction Method based on Novel Causal Convolution Lightweight Transformer Network
  DOI :  10.2352/J.ImagingSci.Technol.2025.69.1.010402  Published OnlineJanuary 2025
Abstract
Abstract

Accurately predicting the remaining shelf life can effectively reduce the risk of spoilage during the storage process of agricultural products. The quality of agricultural products can be indirectly indicated by changes in environmental parameters. To better explore the intrinsic relationship between key environmental parameters during banana storage and their remaining shelf life, this paper proposes a novel causal convolution lightweight Transformer network. This model utilizes causal convolution operations to mine the temporal features of sensor data and applies positional encoding to the input signals. It employs a Transformer encoder to extract and fuse features while also utilizing a probabilistic sparse self-attention mechanism instead of the conventional self-attention mechanism. Moreover, a distillation operation is introduced, which effectively reduces the number of trainable parameters in the Transformer-based model and shortens the training time. Compared to traditional machine learning algorithms (BP, SVM) and conventional time series data mining algorithms (LSTM, RNN), the proposed prediction method achieves a mean squared error of 0.0221, a root mean squared error of 0.1486, a mean absolute error of 0.1101, and a maximum prediction error of 0.2221 days, allowing for more accurate and efficient predictions of bananas’ remaining shelf life.

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  Cite this article 

Yuan Zhang, Xin Li, Xiao Xing, Lei Zhu, Yanping Du, "Banana Shelf Life Prediction Method based on Novel Causal Convolution Lightweight Transformer Networkin Journal of Imaging Science and Technology,  2025,  pp 1 - 8,  https://doi.org/10.2352/J.ImagingSci.Technol.2025.69.1.010402

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Copyright © Society for Imaging Science and Technology 2025
  Article timeline 
  • received May 2024
  • accepted September 2024
  • PublishedJanuary 2025

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