Reference health

Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network

https://doi.org/10.1071/cp21710
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39/39 checkable references clean · checked 2026-08-28

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

6 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 39 checked references that resolve
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The 6 references without a DOI — listed, not checked
no DOI — not checkedAustralian Department of Agriculture, Water and the Environment (2021) Plant pests and diseases @ONLINE. Available at https://www.awe.gov.au/biosecurity-trade/pests-diseases-weeds/plant
no DOI — not checkedAutomatic trap for moth detection in integrated pest management.
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no DOI — not checkedRen S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In ‘Proceedings of the advances in neural information processing systems 28’. pp. 91–99. (Curran Associates)
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