Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks
Abstract 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 integra...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Food |
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| Online Access: | https://doi.org/10.1007/s44187-025-00368-9 |
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| author | Ayan Sar Tanupriya Choudhury Tanmay Sarkar Ketan Kotecha |
| author_facet | Ayan Sar Tanupriya Choudhury Tanmay Sarkar Ketan Kotecha |
| author_sort | Ayan Sar |
| collection | DOAJ |
| description | Abstract 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 |
| format | Article |
| id | doaj-art-f09121b77dbc41c48b86aa88b2c092d3 |
| institution | DOAJ |
| issn | 2731-4286 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Food |
| spelling | doaj-art-f09121b77dbc41c48b86aa88b2c092d32025-08-20T03:18:28ZengSpringerDiscover Food2731-42862025-04-015111810.1007/s44187-025-00368-9Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networksAyan Sar0Tanupriya Choudhury1Tanmay Sarkar2Ketan Kotecha3School of Computer Science, University of Petroleum and Energy Studies (UPES)School of Computer Science, University of Petroleum and Energy Studies (UPES)Department of Food Processing Technology, Malda Polytechnic, West Bengal State Council of Technical Education, Government of West BengalSymbiosis Institute of Technology, Symbiosis International (Deemed University)Abstract 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 Abstracthttps://doi.org/10.1007/s44187-025-00368-9AttentionFreshness classificationPapayaTransformer NetworksDeep Learning |
| spellingShingle | Ayan Sar Tanupriya Choudhury Tanmay Sarkar Ketan Kotecha Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks Discover Food Attention Freshness classification Papaya Transformer Networks Deep Learning |
| title | Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks |
| title_full | Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks |
| title_fullStr | Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks |
| title_full_unstemmed | Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks |
| title_short | Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks |
| title_sort | papayafreshnet a hybrid deep learning framework for non destructive freshness classification of papayas using convolutional and transformer networks |
| topic | Attention Freshness classification Papaya Transformer Networks Deep Learning |
| url | https://doi.org/10.1007/s44187-025-00368-9 |
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