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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...

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Autores principales: Aktar, Rumana, Kharismawati, Dewi Endah, Palaniappan, Kannappan, Aliakbarpour, Hadi, Bunyak, Filiz, Stapleton, Ann E., Kazic, Toni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
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.
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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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