Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies
Morphological classification is the gold standard for identifying necrophilous flies, but its complexity and the scarcity of experts make accurate classification challenging. The development of artificial intelligence for autonomous recognition holds promise as a new approach to improve the efficien...
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MDPI AG
2025-05-01
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| Series: | Insects |
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| Online Access: | https://www.mdpi.com/2075-4450/16/5/529 |
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| author | Yixin Ma Lin Niu Bo Wang Dianxin Li Yanzhu Gao Shan Ha Boqing Fan Yixin Xiong Bin Cong Jianhua Chen Jianqiang Deng |
| author_facet | Yixin Ma Lin Niu Bo Wang Dianxin Li Yanzhu Gao Shan Ha Boqing Fan Yixin Xiong Bin Cong Jianhua Chen Jianqiang Deng |
| author_sort | Yixin Ma |
| collection | DOAJ |
| description | Morphological classification is the gold standard for identifying necrophilous flies, but its complexity and the scarcity of experts make accurate classification challenging. The development of artificial intelligence for autonomous recognition holds promise as a new approach to improve the efficiency and accuracy of fly morphology identification. In our previous study, we developed a GLB-ViT (Global–Local Balanced Vision Transformer)-based deep learning model for fly species identification, which demonstrated improved identification capabilities. To expand the model’s application scope to meet the practical needs of forensic science, we extended the model based on the forensic science practice scenarios, increased the database of identifiable sarcosaphagous fly species, and successfully developed a WeChat Mini Program based on the model. The results show that the model can achieve fast and effective identification of ten common sarcosaphagous flies in Hainan, and the overall correct rate reaches 94.00%. For the few cases of identification difficulties and suspicious results, we have also constructed a rapid molecular species identification system based on DNA Barcoding technology to achieve accurate species identification of the flies under study. As the local fly database continues to be improved, the model is expected to be applicable to local forensic practice. |
| format | Article |
| id | doaj-art-2f6bf6fa51a04c37b09fb29d3278ce19 |
| institution | Kabale University |
| issn | 2075-4450 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Insects |
| spelling | doaj-art-2f6bf6fa51a04c37b09fb29d3278ce192025-08-20T03:47:57ZengMDPI AGInsects2075-44502025-05-0116552910.3390/insects16050529Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous FliesYixin Ma0Lin Niu1Bo Wang2Dianxin Li3Yanzhu Gao4Shan Ha5Boqing Fan6Yixin Xiong7Bin Cong8Jianhua Chen9Jianqiang Deng10Hainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaSchool of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, ChinaHainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaHainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaSchool of Biomedical Informatics and Engineering, Hainan Medical University, Haikou 571199, ChinaSchool of Public Health, Hainan Medical University, Haikou 571199, ChinaHainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaHainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaDepartment of Forensic Medicine, Hebei Medical University, Shijiazhuang 050011, ChinaHainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaHainan Provincial Tropical Forensic Engineering Research Center & Hainan Provincial Academician Workstation (Tropical Forensic Medicine), Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou 571199, ChinaMorphological classification is the gold standard for identifying necrophilous flies, but its complexity and the scarcity of experts make accurate classification challenging. The development of artificial intelligence for autonomous recognition holds promise as a new approach to improve the efficiency and accuracy of fly morphology identification. In our previous study, we developed a GLB-ViT (Global–Local Balanced Vision Transformer)-based deep learning model for fly species identification, which demonstrated improved identification capabilities. To expand the model’s application scope to meet the practical needs of forensic science, we extended the model based on the forensic science practice scenarios, increased the database of identifiable sarcosaphagous fly species, and successfully developed a WeChat Mini Program based on the model. The results show that the model can achieve fast and effective identification of ten common sarcosaphagous flies in Hainan, and the overall correct rate reaches 94.00%. For the few cases of identification difficulties and suspicious results, we have also constructed a rapid molecular species identification system based on DNA Barcoding technology to achieve accurate species identification of the flies under study. As the local fly database continues to be improved, the model is expected to be applicable to local forensic practice.https://www.mdpi.com/2075-4450/16/5/529forensic entomologyArtificial Intelligencesarcosaphagous fliesspecies identificationfine-grained image classification |
| spellingShingle | Yixin Ma Lin Niu Bo Wang Dianxin Li Yanzhu Gao Shan Ha Boqing Fan Yixin Xiong Bin Cong Jianhua Chen Jianqiang Deng Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies Insects forensic entomology Artificial Intelligence sarcosaphagous flies species identification fine-grained image classification |
| title | Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies |
| title_full | Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies |
| title_fullStr | Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies |
| title_full_unstemmed | Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies |
| title_short | Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies |
| title_sort | preliminary development of global local balanced vision transformer deep learning with dna barcoding for automated identification and validation of forensic sarcosaphagous flies |
| topic | forensic entomology Artificial Intelligence sarcosaphagous flies species identification fine-grained image classification |
| url | https://www.mdpi.com/2075-4450/16/5/529 |
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