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YOLO-AppleScab: A Deep Learning Approach for Efficient and Accurate Apple Scab Detection in Varied Lighting Conditions Using CARAFE-Enhanced YOLOv7

Plant and fruit diseases significantly impact agricultural economies by diminishing crop quality and yield. Developing precise, automated detection techniques is crucial to minimize losses and drive economic growth. We introduce YOLO-AppleScab, integrating Content-Aware ReAssembly of FEature (<inlin...

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Gardado en:
Principais autores: Joseph Christian Nouaze, Jordane Sikati
Formato: Artigo
Idioma:Inglês
Publicado: MDPI AG 2023-12-01
Series:Biology and Life Sciences Forum
Assuntos:
scab disease
<named-content content-type="inline-formula"><inline-formula> <mml:math id="mm333"> <mml:semantics> <mml:mrow> <mml:mi mathvariant="double-struck">C</mml:mi> <mml:mi mathvariant="double-struck">A</mml:mi> <mml:mi mathvariant="double-struck">R</mml:mi> <mml:mi mathvariant="double-struck">A</mml:mi> <mml:mi mathvariant="double-struck">F</mml:mi> <mml:mi mathvariant="double-struck">E</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula></named-content>
YOLO-AppleScab
mean average precision
average inference per image
disease detection
Acceso en liña:https://www.mdpi.com/2673-9976/30/1/6
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