PoulTrans: a transformer-based model for accurate poultry condition assessment
Abstract Recent advances in deep learning have significantly enhanced the accuracy of poultry image recognition, particularly in assessing poultry conditions. However, developing intuitive decision support tools remain a significant challenge. To address this, we present PoulTrans, an innovative ima...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98078-w |
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| author | Jun Li Bing Yang Junyang Chen Jiaxin Liu Felix Kwame Amevor Guanyu Chen Buyuan Zhang Xiaoling Zhao |
| author_facet | Jun Li Bing Yang Junyang Chen Jiaxin Liu Felix Kwame Amevor Guanyu Chen Buyuan Zhang Xiaoling Zhao |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Abstract Recent advances in deep learning have significantly enhanced the accuracy of poultry image recognition, particularly in assessing poultry conditions. However, developing intuitive decision support tools remain a significant challenge. To address this, we present PoulTrans, an innovative image captioning framework that leverages a Convolutional Neural Network (CNN) integrated with a CSA_Encoder-Transformer architecture to generate detailed poultry status reports. This model incorporates visual features extracted by CNNs into the Channel Spatial Attention Segmentation Encoder (CSA_Encoder), which produces segmented channel and spatial attention outputs. To optimize multi-level attention and improve the semantic precision of the status descriptions, we introduced a Channel Spatial Memory-Guided Transformer (CSMT) and a novel PS-Loss function. The performance of PoulTrans was tested on the PSC-Captions dataset, achieving top scores of 0.501, 0.803, 4.927, 0.608, and 1.882 for the BLEU-4, ROUGE-L, CIDEr, SPICE, and Sm metrics, respectively. Comprehensive analyses and experiments have validated the effectiveness and reliability of our model, providing advanced tools for automated poultry status generation and enhancing the digital experience for poultry farmers. Our code is available at: https://github.com/kong1107800/PoulTrans . |
| format | Article |
| id | doaj-art-8f06fb13b186400683135cd61ed2cf33 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8f06fb13b186400683135cd61ed2cf332025-08-20T02:20:06ZengNature PortfolioScientific Reports2045-23222025-04-0115111910.1038/s41598-025-98078-wPoulTrans: a transformer-based model for accurate poultry condition assessmentJun Li0Bing Yang1Junyang Chen2Jiaxin Liu3Felix Kwame Amevor4Guanyu Chen5Buyuan Zhang6Xiaoling Zhao7College of Information Engineering, Sichuan Agricultural UniversityCollege of Information Engineering, Sichuan Agricultural UniversityCollege of Information Engineering, Sichuan Agricultural UniversityCollege of Information Engineering, Sichuan Agricultural UniversityKey Laboratory of Livestock and Poultry Multi-Omics, College of Animal Science and Technology, Sichuan Agricultural UniversityCollege of Information Engineering, Sichuan Agricultural UniversityCollege of Information Engineering, Sichuan Agricultural UniversityKey Laboratory of Livestock and Poultry Multi-Omics, College of Animal Science and Technology, Sichuan Agricultural UniversityAbstract Recent advances in deep learning have significantly enhanced the accuracy of poultry image recognition, particularly in assessing poultry conditions. However, developing intuitive decision support tools remain a significant challenge. To address this, we present PoulTrans, an innovative image captioning framework that leverages a Convolutional Neural Network (CNN) integrated with a CSA_Encoder-Transformer architecture to generate detailed poultry status reports. This model incorporates visual features extracted by CNNs into the Channel Spatial Attention Segmentation Encoder (CSA_Encoder), which produces segmented channel and spatial attention outputs. To optimize multi-level attention and improve the semantic precision of the status descriptions, we introduced a Channel Spatial Memory-Guided Transformer (CSMT) and a novel PS-Loss function. The performance of PoulTrans was tested on the PSC-Captions dataset, achieving top scores of 0.501, 0.803, 4.927, 0.608, and 1.882 for the BLEU-4, ROUGE-L, CIDEr, SPICE, and Sm metrics, respectively. Comprehensive analyses and experiments have validated the effectiveness and reliability of our model, providing advanced tools for automated poultry status generation and enhancing the digital experience for poultry farmers. Our code is available at: https://github.com/kong1107800/PoulTrans .https://doi.org/10.1038/s41598-025-98078-wDeep learningPoultry stateImage captionTransformerPoultrans |
| spellingShingle | Jun Li Bing Yang Junyang Chen Jiaxin Liu Felix Kwame Amevor Guanyu Chen Buyuan Zhang Xiaoling Zhao PoulTrans: a transformer-based model for accurate poultry condition assessment Scientific Reports Deep learning Poultry state Image caption Transformer Poultrans |
| title | PoulTrans: a transformer-based model for accurate poultry condition assessment |
| title_full | PoulTrans: a transformer-based model for accurate poultry condition assessment |
| title_fullStr | PoulTrans: a transformer-based model for accurate poultry condition assessment |
| title_full_unstemmed | PoulTrans: a transformer-based model for accurate poultry condition assessment |
| title_short | PoulTrans: a transformer-based model for accurate poultry condition assessment |
| title_sort | poultrans a transformer based model for accurate poultry condition assessment |
| topic | Deep learning Poultry state Image caption Transformer Poultrans |
| url | https://doi.org/10.1038/s41598-025-98078-w |
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