Evidence map›Paper›PMID 39469303›Full record

ArticleFrontiers in robotics and AI2024

Targeted weed management of Palmer amaranth using robotics and deep learning (YOLOv7).

Amlan Balabantaray, Shaswati Behera, CheeTown Liew, Nipuna Chamara, Mandeep Singh, Amit J Jhala, Santosh Pitla

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

7 authors.

Amlan Balabantaray *Machine Automation and Agricultural Robotics Laboratory, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.
Shaswati Behera *Machine Automation and Agricultural Robotics Laboratory, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.
CheeTown LiewMachine Automation and Agricultural Robotics Laboratory, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.
Nipuna ChamaraMachine Automation and Agricultural Robotics Laboratory, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.
Mandeep SinghThe Jhala Weed Management Laboratory, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States.
Amit J JhalaThe Jhala Weed Management Laboratory, Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States.
Santosh PitlaMachine Automation and Agricultural Robotics Laboratory, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective weed management is a significant challenge in agronomic crops which necessitates innovative solutions to reduce negative environmental impacts and minimize crop damage. Traditional methods often rely on indiscriminate herbicide application, which lacks precision and sustainability. To address this critical need, this study demonstrated an AI-enabled robotic system, Weeding robot, designed for targeted weed management. Palmer amaranth (

Indexed as

artificial intelligencedeep learningmachine visionreal timerobotics and automation in agriculturetargeted sprayingweed managementYOLOv7

Identifiers

PMID39469303
PMCPMC11513266

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.