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A novel semi-supervised framework for UAV based crop/weed classification
Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control w...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109769/ https://www.ncbi.nlm.nih.gov/pubmed/33970938 http://dx.doi.org/10.1371/journal.pone.0251008 |
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author | Khan, Shahbaz Tufail, Muhammad Khan, Muhammad Tahir Khan, Zubair Ahmad Iqbal, Javaid Alam, Mansoor |
author_facet | Khan, Shahbaz Tufail, Muhammad Khan, Muhammad Tahir Khan, Zubair Ahmad Iqbal, Javaid Alam, Mansoor |
author_sort | Khan, Shahbaz |
collection | PubMed |
description | Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time. |
format | Online Article Text |
id | pubmed-8109769 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81097692021-05-21 A novel semi-supervised framework for UAV based crop/weed classification Khan, Shahbaz Tufail, Muhammad Khan, Muhammad Tahir Khan, Zubair Ahmad Iqbal, Javaid Alam, Mansoor PLoS One Research Article Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time. Public Library of Science 2021-05-10 /pmc/articles/PMC8109769/ /pubmed/33970938 http://dx.doi.org/10.1371/journal.pone.0251008 Text en © 2021 Khan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Alam, Mansoor A novel semi-supervised framework for UAV based crop/weed classification |
title | A novel semi-supervised framework for UAV based crop/weed classification |
title_full | A novel semi-supervised framework for UAV based crop/weed classification |
title_fullStr | A novel semi-supervised framework for UAV based crop/weed classification |
title_full_unstemmed | A novel semi-supervised framework for UAV based crop/weed classification |
title_short | A novel semi-supervised framework for UAV based crop/weed classification |
title_sort | novel semi-supervised framework for uav based crop/weed classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109769/ https://www.ncbi.nlm.nih.gov/pubmed/33970938 http://dx.doi.org/10.1371/journal.pone.0251008 |
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