By aligning three-dimensional point clouds of the same trees across consecutive growing seasons, the team was able to accurately identify which shoots were removed during pruning and how these decisions relate to annual shoot growth.
Pear trees are widely cultivated across temperate regions, and dormant pruning is essential for maintaining canopy structure, balancing vegetative and reproductive growth, and preventing problems such as poor light penetration or alternate bearing. However, pruning remains one of the most expensive orchard operations, accounting for a substantial share of annual production costs. While mechanized pruning tools exist, they often rely on non-selective cutting, which can reduce fruit yield and quality. Intelligent, selective pruning requires detailed knowledge of tree structure and shoot growth—information that has been difficult to obtain in complex canopies using conventional imaging or manual measurements.
A study (DOI:10.1016/j.plaphe.2025.100136) published in Plant Phenomics on 13 November 2025 by Yue Mu's team, Nanjing Agricultural University, provides a critical foundation for intelligent and automated pruning systems, offering new opportunities to reduce labor dependence while improving precision and consistency in orchard management.
Using repeated 3D point cloud acquisitions of the same pear trees before and after pruning and across consecutive growing seasons, the researchers first applied a two-step alignment strategy—coarse registration followed by fine Iterative Closest Point (ICP) optimization—to precisely align paired point clouds, with alignment accuracy quantified by root mean square error (RMSE). After removing overlapping regions, shoots were segmented using density-based clustering, and shoot architectural parameters, including shoot number, length, and angle, were automatically extracted and validated against manual measurements. The alignment results showed that average RMSE across whole trees was reduced to 0.032 m after registration, with no significant differences between cultivars but clear differences among tree architectures, where the "2 + 1" architecture achieved the highest accuracy (RMSE = 0.025 m). Registration accuracy was also strongly influenced by the time interval between scans, with point clouds collected shortly before and after pruning showing significantly lower errors than those separated by a full year of natural growth. The extracted shoot parameters closely matched manual measurements, with strong correlations for shoot number, total shoot length, shoot angle, and individual shoot length (R² = 0.82–0.92), demonstrating the robustness of the method despite some segmentation errors in densely packed canopies. Applying this pipeline to analyze pruning outcomes revealed that tree architecture, rather than cultivar, was the dominant factor shaping pruning characteristics, influencing pruned shoot number, individual shoot length, canopy volume, and shoot length density. Spatial overlap analysis further showed that most pruned shoots corresponded to annual (one-year-old) shoots, whose angle and length distributions closely matched those of pruned shoots, indicating that pruning decisions largely reflect annual shoot growth patterns. Quantitatively, annual shoots accounted for about 79% of pruned shoot number and over 92% of total pruning length, with the majority exhibiting large inclination angles. Overall, manual dormant pruning followed a highly consistent pattern across years, primarily targeting upright annual shoots through thinning, while a smaller proportion of perennial shoots was selectively removed to maintain canopy structure.
By quantifying what experienced pruners do intuitively, this research provides a practical foundation for automated pruning. Instead of attempting to evaluate every branch in a complex canopy, intelligent systems could focus primarily on identifying annual shoots with specific angle and length characteristics. This simplification could greatly accelerate the development of robotic pruning tools, reduce labor costs, and improve consistency in orchard management.
