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
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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Auteurs

Nooshin Shahbazi (N)

UWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.
Australian Herbicide Resistance Initiative, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

Michael B Ashworth (MB)

UWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.
Australian Herbicide Resistance Initiative, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

J Nikolaus Callow (JN)

UWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

Ajmal Mian (A)

UWA School of Computer Science and Software Engineering, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

Hugh J Beckie (HJ)

UWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.
Australian Herbicide Resistance Initiative, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

Stuart Speidel (S)

Stealth Technologies, 138 Churchill Avenue, Subiaco, WA 6008, Australia.

Elliot Nicholls (E)

Stealth Technologies, 138 Churchill Avenue, Subiaco, WA 6008, Australia.

Ken C Flower (KC)

UWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.
UWA Institute of Agriculture, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

Classifications MeSH