دورية أكاديمية
أعرض في EDS

Computer vision for detecting field-evolved lepidopteran resistance to Bt maize.

التفاصيل البيبلوغرافية
العنوان: Computer vision for detecting field-evolved lepidopteran resistance to Bt maize.
المؤلفون: Dorman SJ; Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA., Kudenov MW; Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA., Lytle AJ; Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA., Griffith EH; Department of Statistics, North Carolina State University, Raleigh, NC, USA., Huseth AS; Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA.
المصدر: Pest management science [Pest Manag Sci] 2021 Nov; Vol. 77 (11), pp. 5236-5245. Date of Electronic Publication: 2021 Aug 05.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Published for SCI by Wiley Country of Publication: England NLM ID: 100898744 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1526-4998 (Electronic) Linking ISSN: 1526498X NLM ISO Abbreviation: Pest Manag Sci Subsets: MEDLINE
أسماء مطبوعة: Original Publication: West Sussex, UK : Published for SCI by Wiley, c2000-
مواضيع طبية MeSH: Bacillus thuringiensis*/genetics , Moths*/genetics, Animals ; Bacterial Proteins/genetics ; Computers ; Endotoxins/genetics ; Hemolysin Proteins/genetics ; Humans ; Insecticide Resistance/genetics ; Pest Control, Biological ; Plants, Genetically Modified/genetics ; Zea mays/genetics
مستخلص: Background: Resistance evolution of lepidopteran pests to Bacillus thuringiensis (Bt) toxins produced in maize and cotton is a significant issue worldwide. Effective toxin stewardship requires reliable detection of field-evolved resistance to enable the implementation of mitigation strategies. Currently, visual estimates of maize injury are used to document changing susceptibility. In this study, we evaluated an existing maize injury monitoring protocol used to estimate Bt resistance levels in Helicoverpa zea (Lepidoptera: Noctuidae).
Results: We detected high interobserver variability across multiple injury metrics, suggesting that the precision and accuracy of maize injury detection could be improved. To do this, we developed a computer vision-based algorithm to measure H. zea injury. Algorithm estimates were more accurate and precise than a sample of human observers. Moreover, observer estimates tended to overpredict H. zea injury, which may increase the false-positive rate, leading to prophylactic insecticide application and unnecessary regulatory action.
Conclusions: Automated detection and tracking of lepidopteran resistance evolution to Bt toxins are critical for genetically engineered crop stewardship to prevent the use of additional insecticides to combat resistant pests. Advantages of this computerized screening are: (i) standardized Bt injury metrics in space and time, (ii) preservation of digital data for cross-referencing when thresholds are reached, and (iii) the ability to increase sample sizes significantly. This technological solution represents a significant step toward improving confidence in resistance monitoring efforts among researchers, regulators and the agricultural biotechnology industry.
(© 2021 Society of Chemical Industry.)
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معلومات مُعتمدة: Biotechnology Risk Assessment competitive grant no. 2020-33522-32272; Crop Protection and Pest Management competitive grant no. 2017-70006-27205
فهرسة مساهمة: Keywords: Bt crops; Helicoverpa zea; corn earworm; engineering application; resistance detection
المشرفين على المادة: 0 (Bacterial Proteins)
0 (Endotoxins)
0 (Hemolysin Proteins)
تواريخ الأحداث: Date Created: 20210726 Date Completed: 20211007 Latest Revision: 20211007
رمز التحديث: 20240628
DOI: 10.1002/ps.6566
PMID: 34310008
قاعدة البيانات: MEDLINE
الوصف
تدمد:1526-4998
DOI:10.1002/ps.6566