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March 3, 2026Tehnicki vjesnik - Technical GazetteOpen Access

Enhanced YOLO Architecture with Attention Mechanism for Accurate Tobacco Plant Counting from UAV Images

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Authors

CMChuanzhi MaYLYuehan LiYPYilong Peng

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Overview

Improves tobacco plant counting accuracy using an optimized YOLO model with attention mechanisms in agriculture.

Key Points

  • The research aims to optimize the YOLO deep learning model for accurate tobacco plant identification in UAV images.
  • Utilized 1200 UAV images from tobacco fields in various growth stages as a training dataset.
  • Trained object detection models, including YOLO v3, with 200 iterations for performance evaluation.
  • Developed an improved YOLO v5-EN model incorporating a channel attention mechanism and other advancements.
  • The improved YOLO v5 model achieved a precision increase of 0.36% and recall increase of 1.55%, with an overall recognition accuracy of 91.41%.
  • The enhanced YOLO v7 model reached a precision of 99.16% and a mean average precision of 95.86%.
  • The modifications effectively addressed missed and false detections, improving detection performance and speed.

Cite This Study

Ma et al. (2026) studied this question.

synapsesocial.com/papers/69a67ec3f353c071a6f0a432https://doi.org/10.17559/tv-20250711002819
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