Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery

Kasper Johansen, Qibin Duan, Yu-Hsuan Tu, Chris Searle, Yu Hsuan Tu, Stuart Phinn, Andrew Robson, Matthew McCabe

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Australia is one of the world’s largest producers of macadamia nuts. As macadamia trees can take up to 15 years to mature and produce maximum yield, it is important to optimize tree condition. Field based assessment of macadamia tree condition is time-consuming and often inconsistent. Using remotely sensed imagery may allow for faster, more extensive, and more consistent assessment of macadamia tree condition. To identify individual macadamia tree crowns, high spatial resolution imagery is required. Hence, the objective of this work was to develop and test an approach to map the condition of individual macadamia tree crowns using both multispectral Unmanned Aerial Vehicle (UAV) and WorldView-3 imagery for different macadamia varieties and three different sites located near Bundaberg, Australia. A random forest classifier, based on all available spectral bands and selected vegetation indices was used to predict five condition categories, ranging from excellent (category 1) to poor (category 5). Various combinations of the developed models were tested between the three sites and over time. The results showed that the multi-spectral WorldView-3 imagery produced the lowest out of bag (OOB) classification errors in most cases. However, for both the UAV and the WorldView-3 imagery, more than 98.5% of predicted macadamia condition categories were either correctly mapped or offset by a single category out of the five condition categories (excellent, good, moderate, fair and poor) for trees of the same variety and at one point in time. Multi-temporally, the WorldView-3 imagery performed better than the UAV data for predicting the condition of the same macadamia tree variety. Applying a model from one site to another site with the same macadamia tree variety produced OOB classification between 31.20 and 42.74%, but with > 98.63% of trees predicted within a single condition category. Importantly, models trained based on one type of macadamia tree variety could not be successfully applied to a site with another variety. The developed classification models may be used as a decision and management support tool for the macadamia industry to inform management practices and improve on-demand irrigation, fertilization, and pest inspection at the individual tree level.
Original languageEnglish (US)
Pages (from-to)28-40
Number of pages13
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - May 20 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was funded by the Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program and Horticulture Innovation Australia Ltd., grant number ST15002 “Multi-Scale Monitoring Tools for Managing Australian Tree Crops – Industry Meets Innovation”. We would like to acknowledge the support of the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), under grant number CE140100049. We also acknowledge the farmers at the Welcome Creek and 1019 Moore Park Road macadamia sites in Bundaberg for allowing us access to collect field and UAV data. Prof Matthew F. McCabe and Dr Kasper Johansen were supported by KAUST.


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