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Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture.

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Title: Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture.
Authors: Malecha, Ziemowit1, ziemowit.malecha@pwr.edu.pl, Ożarowski, Kajetan1,2, Siemasz, Rafał1,3, Chorowski, Maciej1, Tomczuk, Krzysztof2, Strochalska, Bernadeta3, Wondołowska-Grabowska, Anna3
Source: Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 2, p1075, 15p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
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PubType: Academic Journal
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  Data: Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture.
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        Value: 10.3390/app16021075
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        Text: English
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      – TitleFull: Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture.
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              Text: Jan2026
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              Y: 2026
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