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Assessing the Capability and Potential of LiDAR for Weed Detection

Conventional methods of uniformly spraying fields to combat weeds, requires large herbicide inputs at significant cost with impacts on the environment. More focused weed control methods such as site-specific weed management (SSWM) have become popular but require methods to identify weed locations. A...

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Autores principales: Shahbazi, Nooshin, Ashworth, Michael B., Callow, J. Nikolaus, Mian, Ajmal, Beckie, Hugh J., Speidel, Stuart, Nicholls, Elliot, Flower, Ken C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038051/
https://www.ncbi.nlm.nih.gov/pubmed/33810604
http://dx.doi.org/10.3390/s21072328
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author Shahbazi, Nooshin
Ashworth, Michael B.
Callow, J. Nikolaus
Mian, Ajmal
Beckie, Hugh J.
Speidel, Stuart
Nicholls, Elliot
Flower, Ken C.
author_facet Shahbazi, Nooshin
Ashworth, Michael B.
Callow, J. Nikolaus
Mian, Ajmal
Beckie, Hugh J.
Speidel, Stuart
Nicholls, Elliot
Flower, Ken C.
author_sort Shahbazi, Nooshin
collection PubMed
description Conventional methods of uniformly spraying fields to combat weeds, requires large herbicide inputs at significant cost with impacts on the environment. More focused weed control methods such as site-specific weed management (SSWM) have become popular but require methods to identify weed locations. Advances in technology allows the potential for automated methods such as drone, but also ground-based sensors for detecting and mapping weeds. In this study, the capability of Light Detection and Ranging (LiDAR) sensors were assessed to detect and locate weeds. For this purpose, two trials were performed using artificial targets (representing weeds) at different heights and diameter to understand the detection limits of a LiDAR. The results showed the detectability of the target at different scanning distances from the LiDAR was directly influenced by the size of the target and its orientation toward the LiDAR. A third trial was performed in a wheat plot where the LiDAR was used to scan different weed species at various heights above the crop canopy, to verify the capacity of the stationary LiDAR to detect weeds in a field situation. The results showed that 100% of weeds in the wheat plot were detected by the LiDAR, based on their height differences with the crop canopy.
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spelling pubmed-80380512021-04-12 Assessing the Capability and Potential of LiDAR for Weed Detection Shahbazi, Nooshin Ashworth, Michael B. Callow, J. Nikolaus Mian, Ajmal Beckie, Hugh J. Speidel, Stuart Nicholls, Elliot Flower, Ken C. Sensors (Basel) Article Conventional methods of uniformly spraying fields to combat weeds, requires large herbicide inputs at significant cost with impacts on the environment. More focused weed control methods such as site-specific weed management (SSWM) have become popular but require methods to identify weed locations. Advances in technology allows the potential for automated methods such as drone, but also ground-based sensors for detecting and mapping weeds. In this study, the capability of Light Detection and Ranging (LiDAR) sensors were assessed to detect and locate weeds. For this purpose, two trials were performed using artificial targets (representing weeds) at different heights and diameter to understand the detection limits of a LiDAR. The results showed the detectability of the target at different scanning distances from the LiDAR was directly influenced by the size of the target and its orientation toward the LiDAR. A third trial was performed in a wheat plot where the LiDAR was used to scan different weed species at various heights above the crop canopy, to verify the capacity of the stationary LiDAR to detect weeds in a field situation. The results showed that 100% of weeds in the wheat plot were detected by the LiDAR, based on their height differences with the crop canopy. MDPI 2021-03-26 /pmc/articles/PMC8038051/ /pubmed/33810604 http://dx.doi.org/10.3390/s21072328 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Shahbazi, Nooshin
Ashworth, Michael B.
Callow, J. Nikolaus
Mian, Ajmal
Beckie, Hugh J.
Speidel, Stuart
Nicholls, Elliot
Flower, Ken C.
Assessing the Capability and Potential of LiDAR for Weed Detection
title Assessing the Capability and Potential of LiDAR for Weed Detection
title_full Assessing the Capability and Potential of LiDAR for Weed Detection
title_fullStr Assessing the Capability and Potential of LiDAR for Weed Detection
title_full_unstemmed Assessing the Capability and Potential of LiDAR for Weed Detection
title_short Assessing the Capability and Potential of LiDAR for Weed Detection
title_sort assessing the capability and potential of lidar for weed detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038051/
https://www.ncbi.nlm.nih.gov/pubmed/33810604
http://dx.doi.org/10.3390/s21072328
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