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...
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2021-01-01
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Online Access: | https://doi.org/10.1371/journal.pone.0249436 |
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doaj-54ed6eb138fb49e2a2d8f00ef96153c12021-04-11T04:30:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01164e024943610.1371/journal.pone.0249436Real-time recognition of spraying area for UAV sprayers using a deep learning approach.Shahbaz KhanMuhammad TufailMuhammad Tahir KhanZubair Ahmad KhanJavaid IqbalArsalan WasimAgricultural 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.https://doi.org/10.1371/journal.pone.0249436 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shahbaz Khan Muhammad Tufail Muhammad Tahir Khan Zubair Ahmad Khan Javaid Iqbal Arsalan Wasim |
spellingShingle |
Shahbaz Khan Muhammad Tufail Muhammad Tahir Khan Zubair Ahmad Khan Javaid Iqbal Arsalan Wasim Real-time recognition of spraying area for UAV sprayers using a deep learning approach. PLoS ONE |
author_facet |
Shahbaz Khan Muhammad Tufail Muhammad Tahir Khan Zubair Ahmad Khan Javaid Iqbal Arsalan Wasim |
author_sort |
Shahbaz Khan |
title |
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_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_sort |
real-time recognition of spraying area for uav sprayers using a deep learning approach. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2021-01-01 |
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. |
url |
https://doi.org/10.1371/journal.pone.0249436 |
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