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A Track-Type Orchard Mower Automatic Line Switching Decision Model Based on Improved DeepLabV3+

Author

Listed:
  • Lixing Liu

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

  • Pengfei Wang

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

  • Jianping Li

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

  • Hongjie Liu

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

  • Xin Yang

    (College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071000, China)

Abstract

To achieve unmanned line switching operations for a track-type mower in orchards, an automatic line switching decision model based on machine vision has been designed. This model optimizes the structure of the DeepLabV3+ semantic segmentation model, using semantic segmentation data from five stages of the line switching process as the basis for generating navigation paths and adjusting the posture of the track-type mower. The improved model achieved an average accuracy of 91.84% in predicting connected areas of three types of headland environments: freespace, grassland, and leaf. The control system equipped with this model underwent automatic line switching tests for the track-type mower, achieving a success rate of 94% and an average passing time of 12.58 s. The experimental results demonstrate that the improved DeepLabV3+ model exhibits good performance, providing a method for designing automatic line switching control systems for track-type mowers in orchard environments.

Suggested Citation

  • Lixing Liu & Pengfei Wang & Jianping Li & Hongjie Liu & Xin Yang, 2025. "A Track-Type Orchard Mower Automatic Line Switching Decision Model Based on Improved DeepLabV3+," Agriculture, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:6:p:647-:d:1615083
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