Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery

Aaron Aeberli, Stuart Phinn, Kasper Johansen, Andrew Robson, David W. Lamb

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of individual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable individual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of individual banana plants from follower establishment to harvest. Individual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV campaigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of individual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.
Original languageEnglish (US)
Pages (from-to)679
JournalRemote Sensing
Volume15
Issue number3
DOIs
StatePublished - Jan 23 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-01-26
Acknowledgements: This research was funded by Horticulture Innovation and the Department of Agriculture and Water Resources, Australian Government as part of its Rural R&D for Profit Program’s subproject “Multi-Scale Monitoring Tools for Managing Australia Tree Crops-Industry Meets Innovation” (grant RnD4Profit 14-01-008). The authors would like to acknowledge the support from Earle Lawrence (farm holder); Barry Sullivan (Australian Banana Growers Council); fieldwork assistance from Yu-Hsuan Tu and Dan Wu.; scripting support and advice from Yu-Hsuan Tu and Derek Schnieder; D.W.L. acknowledges the support of Food Agility CRC Ltd., funded under the Commonwealth Government CRC Program. The CRC Program supports industry-led collaborations between industry, researchers, and the community.

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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