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YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11

2025-03-18

Abstract excerpt

Detecting pests and diseases on maize leaves under complex conditions, such as varying lighting and occlusion, is challenging, suffering from low detection accuracy and insufficient real-time performance. Hence, this study introduces the lightweight detection method YOLOv11-RepLKNet-CBAM-DynamicHead-WIoU-DynamicATSS (YOLOv11-RCDWD) based on an improved YOLOv11 model. The proposed method builds on the YOLOv11 model...

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Literature Corpus work
923af2b1-ab03-51c2-86b1-5ce92a2556fc
DOI
10.20944/preprints202503.1320.v1
Open publication

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YOLOv11-RCDWD: A New Efficient Model for Detecting Maize Leaf Diseases Based on the Improved YOLOv11DOI 10.20944/preprints202503.1320.v1
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