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Within the agrifood sector, the production of high yields is a driver for UK orchard husbandry. Currently, orchard tree management is typically a non-discriminatory method with all trees subjected to the same interventions. Previous studies indicate that structural complexity of individual orchard trees is an indicator for future yield, which can guide the management of individual trees. However, data on the structure of individual trees is often limited. This study investigated the suitability of using remote sensing methods to capture data that can be used to quantify tree structure. Descriptive metrics based on the mathematical assessment of self-affinity and dimensionality were applied to the remotely-sensed data to quantify tree structure, and were also analysed for suitability as a predictor of fruit yield. The findings suggest that while proximal photogrammetry is informative, terrestrial LiDAR data can be used to quantify structural complexity most effectively and this approach holds greater potential for informing orchard management.

The novel use of proximal photogrammetry and terrestrial LiDAR to quantify the structural complexity of orchard trees