Evidence map›Paper›PMID 33810604›Full record

ArticleSensors (Basel, Switzerland)2021

Assessing the Capability and Potential of LiDAR for Weed Detection.

Nooshin Shahbazi, Michael B Ashworth, J Nikolaus Callow, Ajmal Mian, Hugh J Beckie, Stuart Speidel, Elliot Nicholls, Ken C Flower

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nooshin ShahbaziUWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.ORCID 0000-0001-6348-2164
Michael B AshworthUWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.
J Nikolaus CallowUWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.ORCID 0000-0001-6525-953X
Ajmal MianUWA School of Computer Science and Software Engineering, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.
Hugh J BeckieUWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.ORCID 0000-0002-2659-2265
Stuart SpeidelStealth Technologies, 138 Churchill Avenue, Subiaco, WA 6008, Australia.
Elliot NichollsStealth Technologies, 138 Churchill Avenue, Subiaco, WA 6008, Australia.
Ken C FlowerUWA School of Agriculture and Environment, The University of Western Australia, Crawley, Stirling Highway, WA 6009, Australia.

Funding

Research Training Program (RTP) Fees Offset RTP 2018Research Training Program (RTP) Stipend RTP 2018The Australian Herbicide Resistance Initiative AHRIThe Calenup Postgraduate Research Fund 2019UWA Safety Net Top-Up Scholarship 2018
6 · The paper itself

Abstract

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.

Indexed as

light detection and ranging (LiDAR) sensorsscanning distancetarget orientationtarget sizeweed detection

Identifiers

PMID33810604
PMCPMC8038051

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.