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Article

Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

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Applied Agricultural Remote Sensing Centre, School of Science and Technology, University of New England, Armidale, NSW 2351, Australia
2
Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, St Lucia, QLD 4072, Australia
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Hydrology, Agricultural and Land Observation, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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Food Agility Cooperative Research Centre Ltd., 81 Broadway, Ultimo, NSW 2007, Australia
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Precision Agriculture Research Group, University of New England, Armidale, NSW 2351, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Thomas Alexandridis
Remote Sens. 2021, 13(11), 2123; https://doi.org/10.3390/rs13112123
Received: 26 March 2021 / Revised: 21 May 2021 / Accepted: 25 May 2021 / Published: 28 May 2021
Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management. View Full-Text
Keywords: unoccupied aerial vehicle; UAV; banana plant; geographic object-based image analysis; convolutional neural network; CNN; template matching; local maximum filter unoccupied aerial vehicle; UAV; banana plant; geographic object-based image analysis; convolutional neural network; CNN; template matching; local maximum filter
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MDPI and ACS Style

Aeberli, A.; Johansen, K.; Robson, A.; Lamb, D.W.; Phinn, S. Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sens. 2021, 13, 2123. https://doi.org/10.3390/rs13112123

AMA Style

Aeberli A, Johansen K, Robson A, Lamb DW, Phinn S. Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sensing. 2021; 13(11):2123. https://doi.org/10.3390/rs13112123

Chicago/Turabian Style

Aeberli, Aaron, Kasper Johansen, Andrew Robson, David W. Lamb, and Stuart Phinn. 2021. "Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery" Remote Sensing 13, no. 11: 2123. https://doi.org/10.3390/rs13112123

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