Copy the page URI to the clipboard
Murray, Jon; Fennell, Joseph T.; Blackburn, George Alan; Whyatt, James Duncan and Li, Bo
(2020).
DOI: https://doi.org/10.1007/s11119-019-09676-4
Abstract
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.
Viewing alternatives
Download history
Metrics
Public Attention
Altmetrics from AltmetricNumber of Citations
Citations from DimensionsItem Actions
Export
About
- Item ORO ID
- 82413
- Item Type
- Journal Item
- ISSN
- 1385-2256
- Keywords
- Orchard crop yield; Terrestrial LiDAR; Proximal imaging; Fractal dimension; Correlation dimension
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Environment, Earth and Ecosystem Sciences
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2019 The Authors
- Depositing User
- Joseph Fennell