Multi-spectral imagery captured from drones, also known as remote piloted aircraft systems (RPAS) or unmanned aerial systems (UAS), is becoming increasingly popular for the non-invasive monitoring and managing of various horticultural crops. However, being a highly configurable systems, variations in the way the drone data are collected, corrected and analysed can have serious implications on its accuracies for measuring and predicting parameters of tree structure and condition. Many studies have identified that different data acquisition parameters such as image overlap, flying altitude, radiometric and geometric corrections can influence the integrity of the image data. Yet, little quantification of these effects nor the establishment of best practice for multi-spectral drone imagery data collection has been established.

To address the knowledge gaps, the PhD project developed a workflow that identifies optimal procedures for the acquisition, correction, and extraction of spectral data from a drone when used to measure the heath and structural properties of horticultural tree crops. It consisted three objectives: (1) to determine the optimal data collection protocols for acquiring multi-spectral drone imagery over horticulture tree crops; (2) to determine the optimal correction methods for multi-spectral drone image data for acquiring imagery over horticultural tree crops; and (3) to determine the accuracies of measuring and mapping canopy structural properties and condition of a horticultural tree crop using multi-spectral drone imagery. It fills an essential knowledge gap by determining the basic influences of acquisition and processing protocols on the accuracy of final drone image products. The findings contribute to the fundamental understanding of optimal settings for multi-spectral drone imagery acquisition to maximise the accuracy of output spatial data products to be used for horticulture crop management.

Advisors: Prof Stuart Phinn, Dr Kasper Johansen, A/Prof Andrew Robson

Project members

Yu-Hsuan TU

PhD candidate