Assessing the Capability and Potential of LiDAR for Weed Detection.
light detection and ranging (LiDAR) sensors
scanning distance
target orientation
target size
weed detection
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
26 Mar 2021
26 Mar 2021
Historique:
received:
10
02
2021
revised:
18
03
2021
accepted:
24
03
2021
entrez:
3
4
2021
pubmed:
4
4
2021
medline:
4
4
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33810604
pii: s21072328
doi: 10.3390/s21072328
pmc: PMC8038051
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Research Training Program (RTP) Stipend
ID : RTP 2018
Organisme : UWA Safety Net Top-Up Scholarship
ID : 2018
Organisme : Research Training Program (RTP) Fees Offset
ID : RTP 2018
Organisme : The Australian Herbicide Resistance Initiative
ID : AHRI
Organisme : The Calenup Postgraduate Research Fund
ID : 2019
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