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June 13, 2026AgriEngineeringOpen Access

CB-YOLOv7: A Modified YOLOv7 Approach for Accurate Weed Detection in Complex UAV Imagery from Cotton Fields

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Authors

ADAnindita DasWest Texas A&M UniversityYYYong YangWest Texas A&M UniversityVSVinitha Hannah SubburajWest Texas A&M University

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Implication

Randomized trial demonstrates improved weed detection in agricultural UAV imagery, indicating more efficient crop management.

Key Points

  • The aim is to enhance weed detection accuracy in UAV imagery of cotton fields using a modified YOLOv7 approach.
  • Developed a modified YOLOv7 model incorporating Convolutional Block Attention Module and Bidirectional Feature Pyramid Network.
  • Utilized high-resolution UAV images from rainfed cotton fields, with a dataset of 8396 manually annotated images for training and testing.
  • Evaluated three models: YOLOv7-CBAM, YOLOv7-BiFPN, and combined CB-YOLOv7 for performance assessment.
  • Combined CB-YOLOv7 achieved a mean Average Precision (mAP) of 0.89 at IoU 0.5.
  • An F1-score of 0.84 was reported, indicating high detection accuracy.
  • CBAM increased weed instance detection while BiFPN reduced false positives.

Cite This Study

Das et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf500faef96ed7f057280https://doi.org/10.3390/agriengineering8060235
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