A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees

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.