  In fruit tree growth, pruning is an important management practice for
preventing overcrowding, improving canopy access to light and promoting
regrowth. Due to the slow nature of agriculture, decisions in pruning are
typically made using tradition or rules of thumb rather than data-driven
analysis. Many existing algorithmic, simulation-based approaches rely on
high-fidelity digital captures or purely computer-generated fruit trees, and
are unable to provide specific results on an orchard scale. We present a
framework for suggesting pruning strategies on LiDAR-scanned commercial fruit
trees using a scoring function with a focus on improving light distribution
throughout the canopy. A scoring function to assess the quality of the tree
shape based on its light availability and size was developed for comparative
analysis between trees, and was validated against yield characteristics,
demonstrating a reasonable correlation against fruit count with an $R^2$ score
of 0.615 for avocado and 0.506 for mango. A tool was implemented for simulating
pruning by algorithmically estimating which parts of a tree point cloud would
be removed given specific cut points using structural analysis of the tree,
validated experimentally with an average F1 score of 0.78 across 144
experiments. Finally, new pruning locations were suggested and we used the
previous two stages to estimate the improvement of the tree given these
suggestions. The light distribution was improved by up to 25.15\%,
demonstrating a 16\% improvement over commercial pruning on a real tree, and
certain cut points were discovered which improved light distribution with a
smaller negative impact on tree volume. The final results suggest value in the
framework as a decision making tool for commercial growers, or as a starting
point for automated pruning since the entire process can be performed with
little human intervention.
