Show simple item record

dc.contributor.authorAeberli, Aaron
dc.contributor.authorJohansen, Kasper
dc.contributor.authorRobson, Andrew
dc.contributor.authorLamb, David W.
dc.contributor.authorPhinn, Stuart
dc.identifier.citationAeberli, A., Johansen, K., Robson, A., Lamb, D. W., & Phinn, S. (2021). Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sensing, 13(11), 2123. doi:10.3390/rs13112123
dc.description.abstractUnoccupied 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.
dc.description.sponsorshipThe authors would like to acknowledge the support from Earle Lawrence (farm holder) and Barry Sullivan (Australian Banana Growers Council), and fieldwork assistance from Yu-Hsuan Tu and Dan Wu. 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.
dc.publisherMDPI AG
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.titleDetection of banana plants using multi-temporal multispectral uav imagery
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.identifier.journalRemote Sensing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionApplied Agricultural Remote Sensing Centre, School of Science and Technology, University of New England, Armidale, NSW, 2351, Australia
dc.contributor.institutionRemote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, St Lucia, QLD, 4072, Australia
dc.contributor.institutionFood Agility Cooperative Research Centre Ltd, 81 Broadway, Ultimo, NSW, 2007, Australia
kaust.personJohansen, Kasper

Files in this item

Publisher's version

This item appears in the following Collection(s)

Show simple item record

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.