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Structural parameter determination and pruning pattern analysis of pear tree shoots for dormant pruning.

Jiaqi Li1, Hao Sun1, Gengchen Wu1

  • 1Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Center for Modern Crop Production Co-Sponsored by Province and Ministry, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, 210095, China.

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Summary
This summary is machine-generated.

Automated pear tree pruning relies on understanding dormant pruning patterns. This study developed a high-precision pipeline to quantitatively analyze pruning, revealing annual shoots are the primary targets for thinning.

Keywords:
3D structure analysisAutomated pruningPear tree pruningShoot characters

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Area of Science:

  • Horticulture
  • Agricultural Engineering
  • Computer Vision

Background:

  • Accurate identification of dormant pruning patterns in pear trees is crucial for automated pruning and fruit production.
  • Previous pruning strategy descriptions were qualitative, lacking quantitative analysis due to complex tree architecture.
  • Need for a quantitative method to analyze pruning patterns for developing automated pruning systems.

Purpose of the Study:

  • To propose a high-precision shoot extraction pipeline for quantitative analysis of dormant pruning patterns in pear trees.
  • To analyze the influence of tree architecture and cultivar on pruning patterns.
  • To determine the primary targets and methods of dormant pruning in full-bearing pear trees.

Main Methods:

  • Developed a shoot extraction pipeline using point cloud alignment at different times for quantitative analysis.
  • Characterized structural parameters (shoot number, angle, length, density) of 126 pear trees (two cultivars, three architectures).
  • Validated the pipeline's accuracy with R² values of 0.82 (shoot number), 0.92 (angle), and 0.85 (length).

Main Results:

  • The developed method achieved high accuracy in quantifying shoot parameters.
  • Tree architecture significantly influences pruning patterns more than cultivar, especially in 'Cuiguan' pear.
  • Dormant pruning primarily targets annual shoots (AS), with thinning as the main method (78.62% of pruned shoots by number).

Conclusions:

  • The proposed pipeline enables quantitative analysis of dormant pruning patterns in pear trees.
  • Tree architecture is a key factor in determining pruning strategies.
  • Findings support the development of automated pruning systems for precision orchard management.