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Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8.

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  • 1Macau Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, Macao SAR, China.

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Summary

This study introduces an enhanced YOLOv8 model for accurate strawberry identification, improving recognition in complex environments. The advanced model offers better performance and a lighter structure, ideal for robotic applications.

Keywords:
EfficientNetv2ODConvWise-IoUimproved YOLOv8strawberries recognitiontarget detection

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Machine learning

Background:

  • Strawberry identification is challenging due to environmental diversity and spatial dispersion.
  • Accurate real-time image processing is crucial for agricultural applications like automated harvesting.
  • Existing models may struggle with the complexities of natural growing conditions.

Purpose of the Study:

  • To develop an advanced strawberry recognition model for improved accuracy and efficiency.
  • To enhance the YOLOv8 architecture for better performance in complex agricultural settings.
  • To create a lightweight model suitable for deployment on agricultural robots.

Main Methods:

  • Modified the YOLOv8 architecture by incorporating EfficientNetV2 as the backbone and ODConv.
  • Implemented a dynamic nonmonotonic focusing mechanism for the loss function.
  • Integrated WiseIoU to replace the traditional CIoU loss function.

Main Results:

  • The proposed model achieved significant improvements in mAP50 (16.91%), precision (14.92%), and recall (8.4%) compared to the original YOLOv8.
  • The model's size was reduced by 15.67%, making it more lightweight.
  • Demonstrated superior accuracy in identifying strawberries across different ripeness levels.

Conclusions:

  • The enhanced YOLOv8 model provides a more accurate and efficient solution for strawberry recognition.
  • The model's lightweight design is suitable for real-time processing on picking robots in agricultural settings.
  • This advancement contributes to the practical application of AI in precision agriculture.