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Research on Walnut ( Juglans regia L.) Yield Prediction Based on a Walnut Orchard Point Cloud Model

Author

Listed:
  • Heng Chen

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Jiale Cao

    (School of Technology, China Agricultural University, Beijing 100083, China)

  • Jianshuo An

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Yangjing Xu

    (School of Grassland Science, Beijing Forestry University, Beijing 100083, China)

  • Xiaopeng Bai

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Daochun Xu

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

  • Wenbin Li

    (School of Technology, Beijing Forestry University, Beijing 100083, China
    State Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China
    Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing 100083, China)

Abstract

This study aims to develop a method for predicting walnut ( Juglans regia L.) yield based on the walnut orchard point cloud model, addressing issues such as low efficiency, insufficient accuracy, and high costs in traditional methods. The walnut orchard point cloud is reconstructed using unmanned aerial vehicle (UAV) images, and the semantic segmentation technique is applied to extract the individual walnut tree point cloud model. Furthermore, the tree height, canopy projection area, and volume of each walnut tree are calculated. By combining these morphological features with statistical models and machine learning methods, a prediction model between tree morphology and yield is established, achieving prediction accuracy with a mean absolute error (MAE) of 2.04 kg, a mean absolute percentage error (MAPE) of 17.24%, a root mean square error (RMSE) of 2.81 kg, and a coefficient of determination (R 2 ) of 0.83. This method provides an efficient, accurate, and economically feasible solution for walnut yield prediction, overcoming the limitations of existing technologies.

Suggested Citation

  • Heng Chen & Jiale Cao & Jianshuo An & Yangjing Xu & Xiaopeng Bai & Daochun Xu & Wenbin Li, 2025. "Research on Walnut ( Juglans regia L.) Yield Prediction Based on a Walnut Orchard Point Cloud Model," Agriculture, MDPI, vol. 15(7), pages 1-17, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:7:p:775-:d:1627312
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