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...

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Autores principales:	Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmad Khan, Javaid Iqbal, Mansoor Alam
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5a93bb7460b742fda76bf1f62ead1eee
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spelling	oai:doaj.org-article:5a93bb7460b742fda76bf1f62ead1eee2021-12-02T20:11:24ZA novel semi-supervised framework for UAV based crop/weed classification.1932-620310.1371/journal.pone.0251008https://doaj.org/article/5a93bb7460b742fda76bf1f62ead1eee2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251008https://doaj.org/toc/1932-6203Excessive 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.Shahbaz KhanMuhammad TufailMuhammad Tahir KhanZubair Ahmad KhanJavaid IqbalMansoor AlamPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251008 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shahbaz Khan Muhammad Tufail Muhammad Tahir Khan Zubair Ahmad Khan Javaid Iqbal Mansoor Alam A novel semi-supervised framework for UAV based crop/weed classification.
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	article
author	Shahbaz Khan Muhammad Tufail Muhammad Tahir Khan Zubair Ahmad Khan Javaid Iqbal Mansoor Alam
author_facet	Shahbaz Khan Muhammad Tufail Muhammad Tahir Khan Zubair Ahmad Khan Javaid Iqbal Mansoor Alam
author_sort	Shahbaz Khan
title	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_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_sort	novel semi-supervised framework for uav based crop/weed classification.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5a93bb7460b742fda76bf1f62ead1eee
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A novel semi-supervised framework for UAV based crop/weed classification.

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