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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...
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/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. |
format | Online Article Text |
id | pubmed-8038051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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