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Robust mosaicking of maize fields from aerial imagery
PREMISE: Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high‐throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field‐scale mosaic of the aerial imagery sequence so that the same features are in registration...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507512/ https://www.ncbi.nlm.nih.gov/pubmed/32995105 http://dx.doi.org/10.1002/aps3.11387 |
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author | Aktar, Rumana Kharismawati, Dewi Endah Palaniappan, Kannappan Aliakbarpour, Hadi Bunyak, Filiz Stapleton, Ann E. Kazic, Toni |
author_facet | Aktar, Rumana Kharismawati, Dewi Endah Palaniappan, Kannappan Aliakbarpour, Hadi Bunyak, Filiz Stapleton, Ann E. Kazic, Toni |
author_sort | Aktar, Rumana |
collection | PubMed |
description | PREMISE: Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high‐throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field‐scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. METHODS: We have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two‐dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame‐to‐frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two‐dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (Zea mays) research nurseries freely flown with a variety of trajectories. RESULTS: The VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest‐quality mosaics. DISCUSSION: Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management. |
format | Online Article Text |
id | pubmed-7507512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75075122020-09-28 Robust mosaicking of maize fields from aerial imagery Aktar, Rumana Kharismawati, Dewi Endah Palaniappan, Kannappan Aliakbarpour, Hadi Bunyak, Filiz Stapleton, Ann E. Kazic, Toni Appl Plant Sci Application Articles PREMISE: Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high‐throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field‐scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. METHODS: We have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two‐dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame‐to‐frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two‐dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (Zea mays) research nurseries freely flown with a variety of trajectories. RESULTS: The VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest‐quality mosaics. DISCUSSION: Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management. John Wiley and Sons Inc. 2020-09-10 /pmc/articles/PMC7507512/ /pubmed/32995105 http://dx.doi.org/10.1002/aps3.11387 Text en © 2020 Aktar et al. Applications in Plant Sciences is published by Wiley Periodicals LLC on behalf of the Botanical Society of America This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Application Articles Aktar, Rumana Kharismawati, Dewi Endah Palaniappan, Kannappan Aliakbarpour, Hadi Bunyak, Filiz Stapleton, Ann E. Kazic, Toni Robust mosaicking of maize fields from aerial imagery |
title | Robust mosaicking of maize fields from aerial imagery |
title_full | Robust mosaicking of maize fields from aerial imagery |
title_fullStr | Robust mosaicking of maize fields from aerial imagery |
title_full_unstemmed | Robust mosaicking of maize fields from aerial imagery |
title_short | Robust mosaicking of maize fields from aerial imagery |
title_sort | robust mosaicking of maize fields from aerial imagery |
topic | Application Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507512/ https://www.ncbi.nlm.nih.gov/pubmed/32995105 http://dx.doi.org/10.1002/aps3.11387 |
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