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Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture
This paper describes the development of a modular unmanned aerial vehicle for the detection and eradication of weeds on farmland. Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271433/ https://www.ncbi.nlm.nih.gov/pubmed/34203187 http://dx.doi.org/10.3390/s21134417 |
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author | Ukaegbu, Uchechi F. Tartibu, Lagouge K. Okwu, Modestus O. Olayode, Isaac O. |
author_facet | Ukaegbu, Uchechi F. Tartibu, Lagouge K. Okwu, Modestus O. Olayode, Isaac O. |
author_sort | Ukaegbu, Uchechi F. |
collection | PubMed |
description | This paper describes the development of a modular unmanned aerial vehicle for the detection and eradication of weeds on farmland. Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the problematic weeds using emerging technologies. This research has addressed the aforementioned problem. A quadcopter was built, and components were assembled with light-weight materials. The system consists of the electric motor, electronic speed controller, propellers, frame, lithium polymer (li-po) battery, flight controller, a global positioning system (GPS), and receiver. A sprayer module which consists of a relay, Raspberry Pi 3, spray pump, 12 V DC source, water hose, and the tank was built. It operated in such a way that when a weed is detected based on the deep learning algorithms deployed on the Raspberry Pi, general purpose input/output (GPIO) 17 or GPIO 18 (of the Raspberry Pi) were activated to supply 3.3 V, which turned on a DC relay to spray herbicides accordingly. The sprayer module was mounted on the quadcopter and from the test-running operation conducted, broadleaf and grass weeds were accurately detected and the spraying of herbicides according to the weed type occurred in less than a second. |
format | Online Article Text |
id | pubmed-8271433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82714332021-07-11 Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture Ukaegbu, Uchechi F. Tartibu, Lagouge K. Okwu, Modestus O. Olayode, Isaac O. Sensors (Basel) Article This paper describes the development of a modular unmanned aerial vehicle for the detection and eradication of weeds on farmland. Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the problematic weeds using emerging technologies. This research has addressed the aforementioned problem. A quadcopter was built, and components were assembled with light-weight materials. The system consists of the electric motor, electronic speed controller, propellers, frame, lithium polymer (li-po) battery, flight controller, a global positioning system (GPS), and receiver. A sprayer module which consists of a relay, Raspberry Pi 3, spray pump, 12 V DC source, water hose, and the tank was built. It operated in such a way that when a weed is detected based on the deep learning algorithms deployed on the Raspberry Pi, general purpose input/output (GPIO) 17 or GPIO 18 (of the Raspberry Pi) were activated to supply 3.3 V, which turned on a DC relay to spray herbicides accordingly. The sprayer module was mounted on the quadcopter and from the test-running operation conducted, broadleaf and grass weeds were accurately detected and the spraying of herbicides according to the weed type occurred in less than a second. MDPI 2021-06-28 /pmc/articles/PMC8271433/ /pubmed/34203187 http://dx.doi.org/10.3390/s21134417 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ukaegbu, Uchechi F. Tartibu, Lagouge K. Okwu, Modestus O. Olayode, Isaac O. Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture |
title | Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture |
title_full | Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture |
title_fullStr | Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture |
title_full_unstemmed | Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture |
title_short | Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture |
title_sort | development of a light-weight unmanned aerial vehicle for precision agriculture |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271433/ https://www.ncbi.nlm.nih.gov/pubmed/34203187 http://dx.doi.org/10.3390/s21134417 |
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