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

Abstract

In fruit tree growth, pruning is an important management practice for preventing overcrowding, improving canopy access to light and promoting regrowth. In fruit with a high energy content, including avocado (Persea Americana), ensuring all parts of the canopy have sufficient exposure to light is of particular importance. Due to the slow nature of agriculture and the numerous parameters contributing to yield, decisions in pruning, particularly in selective limb removal, 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. Due to the destructive nature of physical experimentation, this framework is presented using a three-stage approach where stages can be independently validated. Firstly, 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 using observations from agricultural literature, and was validated against yield characteristics from an avocado and mango orchard. This demonstrated a reasonable correlation against fruit count, with an R2 score of 0.615 for avocado and 0.506 for mango. The second stage was to implement a tool 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. This was validated experimentally using manually generated ground truth pruned tree models, showing good results with an average F1 score of 0.78 across 144 experiments. Finally, new pruning locations were suggested by discovering points in the tree which negatively impact the light distribution, and we used the previous two stages to estimate the improvement of the tree given these suggestions. These results were compared to a tree which was commercially pruned using existing wisdom. The light distribution was improved by up to 25.15%, demonstrating a 16% improvement over the commercial pruning, 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. Further development should be performed to improve the suggestion mechanism and incorporate more agricultural objectives and operations.