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Efficient and accurate target detection is the foundation of tomato harvest in greenhouse. In this paper, a disease detection method of tomato unmanned detection robot is proposed, and the improved 3E-yolov5l algorithm is used to realize the accurate detection and recognition of tomato target, maturity and crack. Four different attention modules are integrated into the backbone network of the basic algorithm model to determine the optimal detection performance. After evaluating the mean average precision (mAP@0.5) of the network, in this paper, the ECA attention module is integrated into the C3 network layer for the final test. Application of 3E-yolov5l algorithm on verification set mAP@0.5 It is 94.7%, which is 4.0% higher than that of the yolov5l algorithm, which verifies the effectiveness of attention mechanism in improving the performance of target detection.

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A Disease Detection Method of Tomato Unmanned Inspection Robot

  • 会议论文

  • 发表日期：

    2025年

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  • Xuejie Qiao,
  • Fuhao Yang,
  • Yang Zhang,
  • Xinyi Na,
  • Yanhua Liu,
  • Xiang Yue

摘要

Efficient and accurate target detection is the foundation of tomato harvest in greenhouse. In this paper, a disease detection method of tomato unmanned detection robot is proposed, and the improved 3E-yolov5l algorithm is used to realize the accurate detection and recognition of tomato target, maturity and crack. Four different attention modules are integrated into the backbone network of the basic algorithm model to determine the optimal detection performance. After evaluating the mean average precision (mAP@0.5) of the network, in this paper, the ECA attention module is integrated into the C3 network layer for the final test. Application of 3E-yolov5l algorithm on verification set mAP@0.5 It is 94.7%, which is 4.0% higher than that of the yolov5l algorithm, which verifies the effectiveness of attention mechanism in improving the performance of target detection.