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<p>The freshness of fruits, especially papayas, is essential for consumer satisfaction and marketability. This article introduces PapayaFreshNet, an innovative deep learning network designed for the automatic categorization of papaya freshness through picture analysis. The architecture integrates the advantages of Convolutional Neural Networks (CNNs) with sophisticated Transformer-based feature extraction and attention methods. Utilizing ResNet50 for hierarchical feature extraction, succeeded by a multi-head self-attention Transformer encoder, our model proficiently captures both local and global picture patterns that signify fruit freshness. The model integrates Squeeze-and-Excitation (SE) blocks for channel-wise attention, improving the extraction of the most prominent features. Comprehensive data augmentation methods are employed to reduce overfitting and enhance model generalization. The suggested model attains an impressive training accuracy of 99.65% and a testing accuracy of 97.68%, illustrating its capability in high-precision, real-time freshness classification. The findings indicate that PapayaFreshNet functions as an efficient instrument for non-destructive, automated quality control in agricultural and retail, delivering dependable freshness evaluations to improve supply chain efficacy and minimize waste.</p> Graphical Abstract <p></p>

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Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月14日

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  • Ayan Sar,
  • Tanupriya Choudhury,
  • Tanmay Sarkar,
  • Ketan Kotecha

摘要

The freshness of fruits, especially papayas, is essential for consumer satisfaction and marketability. This article introduces PapayaFreshNet, an innovative deep learning network designed for the automatic categorization of papaya freshness through picture analysis. The architecture integrates the advantages of Convolutional Neural Networks (CNNs) with sophisticated Transformer-based feature extraction and attention methods. Utilizing ResNet50 for hierarchical feature extraction, succeeded by a multi-head self-attention Transformer encoder, our model proficiently captures both local and global picture patterns that signify fruit freshness. The model integrates Squeeze-and-Excitation (SE) blocks for channel-wise attention, improving the extraction of the most prominent features. Comprehensive data augmentation methods are employed to reduce overfitting and enhance model generalization. The suggested model attains an impressive training accuracy of 99.65% and a testing accuracy of 97.68%, illustrating its capability in high-precision, real-time freshness classification. The findings indicate that PapayaFreshNet functions as an efficient instrument for non-destructive, automated quality control in agricultural and retail, delivering dependable freshness evaluations to improve supply chain efficacy and minimize waste.

Graphical Abstract