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Semi-automated identification of biological control agent using artificial intelligence
Liao, Jhih-Rong1; Lee, Hsiao-Chin1; Chiu, Ming-Chih2; Ko, Chiun-Cheng1
Corresponding AuthorChiu, Ming-Chih(mingchih.chiu@gmail.com) ; Ko, Chiun-Cheng(kocc2501@ntu.edu.tw)
2020-09-03
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume10Issue:1Pages:9
AbstractThe accurate identification of biological control agents is necessary for monitoring and preventing contamination in integrated pest management (IPM); however, this is difficult for non-taxonomists to achieve in the field. Many machine learning techniques have been developed for multiple applications (e.g., identification of biological organisms). Some phytoseiids are biological control agents for small pests, such as Neoseiulus barkeri Hughes. To identify a precise biological control agent, a boosting machine learning classification, namely eXtreme Gradient Boosting (XGBoost), was introduced in this study for the semi-automated identification of phytoseiid mites. XGBoost analyses were based on 22 quantitative morphological features among 512 specimens of N. barkeri and related phytoseiid species. These features were extracted manually from photomicrograph of mites and included dorsal and ventrianal shield lengths, setal lengths, and length and width of spermatheca. The results revealed 100% accuracy rating, and seta j4 achieved significant discrimination among specimens. The present study provides a path through which skills and experiences can be transferred between experts and non-experts. This can serve as a foundation for future studies on the automated identification of biological control agents for IPM.
DOI10.1038/s41598-020-71798-x
Funding OrganizationChinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan
Indexed BySCI ; SCI
Language英语
Funding ProjectChinese Academy of Sciences (CAS Taiwan Young Talent Programme)[2017TW2SA0004] ; Ministry of Science and Technology, Taiwan[MOST105-2621-B-002-002-MY3] ; Ministry of Science and Technology, Taiwan[MOST108-2621-B-002-005-MY3]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000571229700124
WOS KeywordAMBLYSEIUS-BARKERI ACARINA ; INTRASPECIFIC VARIATIONS ; SETAL PATTERNS ; DORSAL SHIELD ; MITES ACARI ; PHYTOSEIIDAE ; HUGHES ; THYSANOPTERA ; IMAGE
PublisherNATURE RESEARCH
Funding OrganizationChinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Chinese Academy of Sciences (CAS Taiwan Young Talent Programme) ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan ; Ministry of Science and Technology, Taiwan
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Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/342005/38854
Collection其他_期刊论文
Corresponding AuthorChiu, Ming-Chih; Ko, Chiun-Cheng
Affiliation1.Natl Taiwan Univ, Dept Entomol, Taipei 10617, Taiwan
2.Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
Recommended Citation
GB/T 7714
Liao, Jhih-Rong,Lee, Hsiao-Chin,Chiu, Ming-Chih,et al. Semi-automated identification of biological control agent using artificial intelligence[J]. SCIENTIFIC REPORTS,2020,10(1):9.
APA Liao, Jhih-Rong,Lee, Hsiao-Chin,Chiu, Ming-Chih,&Ko, Chiun-Cheng.(2020).Semi-automated identification of biological control agent using artificial intelligence.SCIENTIFIC REPORTS,10(1),9.
MLA Liao, Jhih-Rong,et al."Semi-automated identification of biological control agent using artificial intelligence".SCIENTIFIC REPORTS 10.1(2020):9.
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