Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery
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 m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2123 |
| _version_ | 1797532267858362368 |
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| author | Aaron Aeberli Kasper Johansen Andrew Robson David W. Lamb Stuart Phinn |
| author_facet | Aaron Aeberli Kasper Johansen Andrew Robson David W. Lamb Stuart Phinn |
| author_sort | Aaron Aeberli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:56:42Z |
| format | Article |
| id | doaj.art-861dafc5f0374dfca28c156f540929d8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:56:42Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-861dafc5f0374dfca28c156f540929d82023-11-21T21:50:22ZengMDPI AGRemote Sensing2072-42922021-05-011311212310.3390/rs13112123Detection of Banana Plants Using Multi-Temporal Multispectral UAV ImageryAaron Aeberli0Kasper Johansen1Andrew Robson2David W. Lamb3Stuart Phinn4Applied Agricultural Remote Sensing Centre, School of Science and Technology, University of New England, Armidale, NSW 2351, AustraliaHydrology, Agricultural and Land Observation, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaApplied Agricultural Remote Sensing Centre, School of Science and Technology, University of New England, Armidale, NSW 2351, AustraliaFood Agility Cooperative Research Centre Ltd., 81 Broadway, Ultimo, NSW 2007, AustraliaRemote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, St Lucia, QLD 4072, AustraliaUnoccupied 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.https://www.mdpi.com/2072-4292/13/11/2123unoccupied aerial vehicleUAVbanana plantgeographic object-based image analysisconvolutional neural networkCNN |
| spellingShingle | Aaron Aeberli Kasper Johansen Andrew Robson David W. Lamb Stuart Phinn Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery Remote Sensing unoccupied aerial vehicle UAV banana plant geographic object-based image analysis convolutional neural network CNN |
| title | Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery |
| title_full | Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery |
| title_fullStr | Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery |
| title_full_unstemmed | Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery |
| title_short | Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery |
| title_sort | detection of banana plants using multi temporal multispectral uav imagery |
| topic | unoccupied aerial vehicle UAV banana plant geographic object-based image analysis convolutional neural network CNN |
| url | https://www.mdpi.com/2072-4292/13/11/2123 |
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