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...

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Veröffentlicht in:PloS one Jg. 16; H. 4; S. e0249436
Hauptverfasser: Khan, Shahbaz, Tufail, Muhammad, Khan, Muhammad Tahir, Khan, Zubair Ahmad, Iqbal, Javaid, Wasim, Arsalan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.04.2021
Public Library of Science (PLoS)
Schlagworte:
Agricultural engineering
Agricultural machinery
Agriculture
Aircraft
Algorithms
Automation
Biology and Life Sciences
Computer and Information Sciences
Crop fields
Crops
Deep Learning
Drone aircraft
Electrical engineering
Engineering
Farm equipment
Foams
Humans
Internet of Things
Laboratories
Localization
Machine learning
Mechanical engineering
Mechatronics
Neural networks
New technology
Object recognition (Computers)
Pattern recognition
People and Places
Pesticides
Remote Sensing Technology - instrumentation
Remote Sensing Technology - methods
Robotics
Software
Spraying
Spraying equipment
Target recognition
Task scheduling
Technology
Technology application
Unmanned aerial vehicles
Weed Control
Pakistan
Rawalpindi Pakistan
Peshawar Pakistan
ISSN:1932-6203, 1932-6203
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Competing Interests: The authors have no competing interests.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0249436