Understanding Growth in Orchards Using TLS-derived Tree Growth Measurements

By Emmanuel Sabla

Abstract
Accurate measurement and analysis of tree structural parameters offers a wide range of benefits for optimizing orchard management practices and enhancing fruit production. The study presented a framework for deriving key apple tree parameters from terrestrial laser scanning (TLS) data and evaluating their growth dynamics across multiple seasons. A systematic literature review following the PRISMA guidelines identified relevant studies that verified the extraction of 12 structural fruit tree components from TLS data. Components such as tree height, crown dimensions, branch features, and derived leaf indices were found to be retrievable from TLS point clouds of various fruit tree species. The study also focused on growth analysis involving repeated TLS scans of an apple orchard spanning the winter, spring, and summer seasons of 2022. Statistical analyses of randomly selected apple tree revealed significant differences in branch length distributions across various branch orders and scanning periods. The study discovered that higher-order branches were shorter in length than lower-order branches, a statistical outcome which was consistent with typical fruit tree structures. This informs pruning strategies recommending that cutting higher-order branches can enhance canopy light and air flow potentially improving fruit yield. Another statistical test also revealed stable growth patterns in lower order branches across various seasons. This demonstrated that conserving lower order branches play an important role in maintaining the structural integrity of apple trees. These results provide useful insights into the complex architecture and growth patterns of apple trees. The study contributed to existing literature by presenting clear methods for estimating and analysing apple tree attributes from TLS data. These outcomes support researchers in correctly assessing apple tree growth. In summary, the study offered a complete approach to leveraging TLS technology for quantitative assessment of apple tree growth, bridging the gap between advanced remote sensing techniques and ecological practices. The methodologies and findings presented in the study have significant implications for optimizing orchard management strategies and promoting sustainable fruit production.