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Real-time recognition of spraying area for UAV sprayers using a deep learning approach

Agricultural production is vital for the stability of the country’s economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators expos...

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Detalles Bibliográficos
Autores principales: Khan, Shahbaz, Tufail, Muhammad, Khan, Muhammad Tahir, Khan, Zubair Ahmad, Iqbal, Javaid, Wasim, Arsalan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016340/
https://www.ncbi.nlm.nih.gov/pubmed/33793634
http://dx.doi.org/10.1371/journal.pone.0249436
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author Khan, Shahbaz
Tufail, Muhammad
Khan, Muhammad Tahir
Khan, Zubair Ahmad
Iqbal, Javaid
Wasim, Arsalan
author_facet Khan, Shahbaz
Tufail, Muhammad
Khan, Muhammad Tahir
Khan, Zubair Ahmad
Iqbal, Javaid
Wasim, Arsalan
author_sort Khan, Shahbaz
collection PubMed
description Agricultural production is vital for the stability of the country’s economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study’s objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying.
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spelling pubmed-80163402021-04-08 Real-time recognition of spraying area for UAV sprayers using a deep learning approach Khan, Shahbaz Tufail, Muhammad Khan, Muhammad Tahir Khan, Zubair Ahmad Iqbal, Javaid Wasim, Arsalan PLoS One Research Article Agricultural production is vital for the stability of the country’s economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (damaging the ecosystem) and the human operators exposed to it. The use of Unmanned Aerial Vehicles (UAVs) has been proposed by several authors in the literature for performing the desired spraying and is considered safer and more precise than the conventional methods. Therefore, the study’s objective was to develop an accurate real-time recognition system of spraying areas for UAVs, which is of utmost importance for UAV-based sprayers. A two-step target recognition system was developed by using deep learning for the images collected from a UAV. Agriculture cropland of coriander was considered for building a classifier for recognizing spraying areas. The developed deep learning system achieved an average F1 score of 0.955, while the classifier recognition average computation time was 3.68 ms. The developed deep learning system can be deployed in real-time to UAV-based sprayers for accurate spraying. Public Library of Science 2021-04-01 /pmc/articles/PMC8016340/ /pubmed/33793634 http://dx.doi.org/10.1371/journal.pone.0249436 Text en © 2021 Khan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Khan, Shahbaz
Tufail, Muhammad
Khan, Muhammad Tahir
Khan, Zubair Ahmad
Iqbal, Javaid
Wasim, Arsalan
Real-time recognition of spraying area for UAV sprayers using a deep learning approach
title Real-time recognition of spraying area for UAV sprayers using a deep learning approach
title_full Real-time recognition of spraying area for UAV sprayers using a deep learning approach
title_fullStr Real-time recognition of spraying area for UAV sprayers using a deep learning approach
title_full_unstemmed Real-time recognition of spraying area for UAV sprayers using a deep learning approach
title_short Real-time recognition of spraying area for UAV sprayers using a deep learning approach
title_sort real-time recognition of spraying area for uav sprayers using a deep learning approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016340/
https://www.ncbi.nlm.nih.gov/pubmed/33793634
http://dx.doi.org/10.1371/journal.pone.0249436
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