Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783673840547659776 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8016340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT khanshahbaz realtimerecognitionofsprayingareaforuavsprayersusingadeeplearningapproach AT tufailmuhammad realtimerecognitionofsprayingareaforuavsprayersusingadeeplearningapproach AT khanmuhammadtahir realtimerecognitionofsprayingareaforuavsprayersusingadeeplearningapproach AT khanzubairahmad realtimerecognitionofsprayingareaforuavsprayersusingadeeplearningapproach AT iqbaljavaid realtimerecognitionofsprayingareaforuavsprayersusingadeeplearningapproach AT wasimarsalan realtimerecognitionofsprayingareaforuavsprayersusingadeeplearningapproach |