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A lightweight detection model for gummosis on tree branches based on an improved YOLO algorithm.

Pingchuan Zhang1, Zeze Ma2, Ying Yang2

  • 1College of Computer Science and Technology, Henan Institute of Science and Technology, Henan, China. zhangpingc@hist.edu.cn.

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A new lightweight YOLO-Gum model accurately detects peach tree gummosis, even on hard-to-see lesions. This advanced detection system improves disease management and supports robotic vision for plant health.

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CCFMPeach gummosisRobotic vision systemsSENetV2Smart agricultureTree branches

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Area of Science:

  • Plant Pathology
  • Computer Vision
  • Agricultural Technology

Background:

  • Gummosis is a prevalent disease affecting stone fruits, particularly peach trees, manifesting on trunks and branches.
  • Direct observation of high branch and trunk lesions is challenging due to complex morphology and low differentiation.
  • Accurate detection is crucial for effective prevention and scientific management of peach gummosis.

Purpose of the Study:

  • To develop a lightweight and accurate detection model for peach tree gummosis.
  • To address limitations in observing and diagnosing gummosis lesions on peach trees.
  • To provide a technical foundation for automated disease detection and management systems.

Main Methods:

  • Integration of the SENetV2 module into the YOLOv8 backbone to enhance feature representation.
  • Introduction of the cross-scale convolutional feature fusion module (CCFM) into the neck structure for improved feature integration and efficiency.
  • Fusion of SENetV2 and CCFM to create the lightweight YOLO-Gum model, optimizing feature extraction and detection accuracy.

Main Results:

  • The improved YOLOv8n model (YOLO-Gum) achieved a precision of 92.5% and an F1 score of 74.3%, outperforming the original YOLOv8n by 5.3% and 6.2%.
  • The model exhibits reduced parameters (2.79 M), smaller size (5.57 MB), and fewer floating-point operations (7.6 G), representing significant efficiency gains.
  • Demonstrated improvements in detection accuracy and computational efficiency compared to the baseline YOLOv8n model.

Conclusions:

  • The developed YOLO-Gum model is lightweight, precise, and robust for detecting peach tree gummosis.
  • This model offers significant improvements in detection performance and computational efficiency.
  • Provides valuable technical support for peach tree health management and robotic disease detection systems.