A novel semi-supervised framework for UAV based crop/weed classification

被引:43
作者
Khan, Shahbaz [1 ,2 ]
Tufail, Muhammad [1 ,2 ]
Khan, Muhammad Tahir [1 ,2 ]
Khan, Zubair Ahmad [1 ]
Iqbal, Javaid [3 ]
Alam, Mansoor [1 ,2 ]
机构
[1] Univ Engn & Technol, Dept Mechatron Engn, Peshawar, Pakistan
[2] Natl Ctr Robot & Automat NCRA, Adv Robot & Automat Lab, Rawalpindi, Pakistan
[3] Natl Univ Sci & Technol NUST, Coll Elect & Mech Engn CEME, Islamabad, Pakistan
来源
PLOS ONE | 2021年 / 16卷 / 05期
关键词
WEED; IDENTIFICATION; NETWORK;
D O I
10.1371/journal.pone.0251008
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:18
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[1]   Improving semi-supervised learning through optimum connectivity [J].
Amorim, Willian P. ;
Falcao, Alexandre X. ;
Papa, Joao P. ;
Carvalho, Marcelo H. .
PATTERN RECOGNITION, 2016, 60 :72-85
[2]   Semi-supervised learning with convolutional neural networks for UAV images automatic recognition [J].
Amorim, Willian Paraguassu ;
Tetila, Everton Castelao ;
Pistori, Hemerson ;
Papa, Joao Paulo .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 164
[3]  
Andrea CC, 2017, 2017 IEEE SECOND ECUADOR TECHNICAL CHAPTERS MEETING (ETCM)
[4]  
[Anonymous], 2017, 1 C ROB LEARN
[5]  
Asnafi M., 2018, Sustainable Structures and Materials, An International Journal, V1, P44, DOI [10.26392/SSM.2018.01.02.044, DOI 10.26392/SSM.2018.01.02.044]
[6]   Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles [J].
Bandini, Filippo ;
Jakobsen, Jakob ;
Olesen, Daniel ;
Reyna-Gutierrez, Jose Antonio ;
Bauer-Gottwein, Peter .
JOURNAL OF HYDROLOGY, 2017, 548 :237-250
[7]  
Bengio Y., ADV NEURAL INFORM PR, P153
[8]   Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize [J].
Castaldi, F. ;
Pelosi, F. ;
Pascucci, S. ;
Casa, R. .
PRECISION AGRICULTURE, 2017, 18 (01) :76-94
[9]  
Dai Z., 2017, 31 C NEUR INF PROC S
[10]   An adaptive approach for UAV-based pesticide spraying in dynamic environments [J].
Faical, Bruno S. ;
Freitas, Heitor ;
Gomes, Pedro H. ;
Mano, Leandro Y. ;
Pessin, Gustavo ;
de Carvalho, Andre C. P. L. F. ;
Krishnamachari, Bhaskar ;
Ueyama, Jo .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2017, 138 :210-223