Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments
Growing global demand for mushrooms in the agricultural, pharmaceutical, and food sectors raises pressing concerns for food safety, as inadequate identification can lead to the inadvertent inclusion of toxic species. Although deep learning offers promising solutions for large-scale classification an...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Applied Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772502225003476 |
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| author | Mengze Du Fei Wang Weibing Yan Jie Guo Lu Liu Ping Lv Yong He Xuping Feng Yuwei Wang |
| author_facet | Mengze Du Fei Wang Weibing Yan Jie Guo Lu Liu Ping Lv Yong He Xuping Feng Yuwei Wang |
| author_sort | Mengze Du |
| collection | DOAJ |
| description | Growing global demand for mushrooms in the agricultural, pharmaceutical, and food sectors raises pressing concerns for food safety, as inadequate identification can lead to the inadvertent inclusion of toxic species. Although deep learning offers promising solutions for large-scale classification and quality control, its effectiveness is often limited by the scarcity of high-quality training datasets and the inherently complex morphological traits of mushrooms. To address these challenges, this study proposes a diffusion model-based data augmentation that preserves essential morphological features while diversifying training samples. By generating realistic images, this approach significantly improves classification accuracy, especially for underrepresented species, and achieves superior results across various image quality indicators. Furthermore, a Top-k recommendation method ranks the most likely classifications, bolstering recognition of rarer species and reducing food safety risks linked to misidentification. Experiments on 110 mushroom species demonstrated a 13.51 % increase in mean recall rate across eight deep learning models, with Top-3 and Top-5 recall rates further improved by 7.56 % and 6.79 %, respectively. These findings highlight the practical utility of diffusion-based augmentation in enhancing model robustness, ensuring more reliable monitoring compared to traditional methods, and mitigating the likelihood of toxic mushrooms entering the food chain. Overall, this study underscores the critical importance of leveraging synthetic data generation to advance mushroom recognition, supporting safer and more sustainable food systems. |
| format | Article |
| id | doaj-art-bb14c84b9b6e4c06b07b76b3d2c7ae8d |
| institution | Kabale University |
| issn | 2772-5022 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Food Research |
| spelling | doaj-art-bb14c84b9b6e4c06b07b76b3d2c7ae8d2025-08-20T03:31:20ZengElsevierApplied Food Research2772-50222025-06-015110103910.1016/j.afres.2025.101039Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environmentsMengze Du0Fei Wang1Weibing Yan2Jie Guo3Lu Liu4Ping Lv5Yong He6Xuping Feng7Yuwei Wang8College of Engineering, Anhui Agricultural University, Hefei 230036, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, ChinaZhejiang Hongye Equipment Technology Co., LTD, Wenling 317500, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Engineering, Anhui Agricultural University, Hefei 230036, ChinaZhejiang SLA Engineering Design Co., LTD, Hangzhou 310000, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Corresponding authors.College of Engineering, Anhui Agricultural University, Hefei 230036, China; Corresponding authors.Growing global demand for mushrooms in the agricultural, pharmaceutical, and food sectors raises pressing concerns for food safety, as inadequate identification can lead to the inadvertent inclusion of toxic species. Although deep learning offers promising solutions for large-scale classification and quality control, its effectiveness is often limited by the scarcity of high-quality training datasets and the inherently complex morphological traits of mushrooms. To address these challenges, this study proposes a diffusion model-based data augmentation that preserves essential morphological features while diversifying training samples. By generating realistic images, this approach significantly improves classification accuracy, especially for underrepresented species, and achieves superior results across various image quality indicators. Furthermore, a Top-k recommendation method ranks the most likely classifications, bolstering recognition of rarer species and reducing food safety risks linked to misidentification. Experiments on 110 mushroom species demonstrated a 13.51 % increase in mean recall rate across eight deep learning models, with Top-3 and Top-5 recall rates further improved by 7.56 % and 6.79 %, respectively. These findings highlight the practical utility of diffusion-based augmentation in enhancing model robustness, ensuring more reliable monitoring compared to traditional methods, and mitigating the likelihood of toxic mushrooms entering the food chain. Overall, this study underscores the critical importance of leveraging synthetic data generation to advance mushroom recognition, supporting safer and more sustainable food systems.http://www.sciencedirect.com/science/article/pii/S2772502225003476Precision agricultureDeep leaningData enhancementMushroom classification |
| spellingShingle | Mengze Du Fei Wang Weibing Yan Jie Guo Lu Liu Ping Lv Yong He Xuping Feng Yuwei Wang Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments Applied Food Research Precision agriculture Deep leaning Data enhancement Mushroom classification |
| title | Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments |
| title_full | Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments |
| title_fullStr | Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments |
| title_full_unstemmed | Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments |
| title_short | Improving food safety: Synthetic data augmentation for accurate mushroom species identification in complex environments |
| title_sort | improving food safety synthetic data augmentation for accurate mushroom species identification in complex environments |
| topic | Precision agriculture Deep leaning Data enhancement Mushroom classification |
| url | http://www.sciencedirect.com/science/article/pii/S2772502225003476 |
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