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Journal of Field Robotics
Article . 2017 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
DBLP
Article . 2020
Data sources: DBLP
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Robot for weed species plant‐specific management

Authors: Owen Bawden; Jason Kulk; Ray Russell; Chris McCool; Andrew English; Feras Dayoub; Christopher F. Lehnert; +1 Authors

Robot for weed species plant‐specific management

Abstract

AbstractThe rapid evolution of herbicide‐resistant weed species has revitalized research in nonchemical methods for weed destruction. Robots with vision‐based capabilities for online weed detection and classification are a key enabling factor for the specialized treatment of individual weed species. This paper describes the design, development, and testing of a modular robotic platform with a heterogeneous weeding array for agriculture. Starting from requirements derived from farmer insights, technical specifications are put forward. A design of a robotic platform is conducted based on the required technical specifications, and a prototype is manufactured and tested. The second part of the paper focuses on the weeding mechanism attached to the robotic platform. This includes aspects of vision for weed detection and classification, as well as the design of a weeding array that combines chemical and mechanical methods for weed destruction. Field trials of the weed detection and classification system show an accuracy of 92.3% across a range of weed species, while the heterogeneous weed management system is able to selectively apply a mechanical or chemical control method based on the species of weed. Together, the robotic platform and weeding array demonstrate the potential for robotic plant‐species–specific weed management enabled by the vision‐based online detection and classification algorithms.

Country
Australia
Keywords

629, machine learning, 571, Agricultural robotics, 600, robotic design, computer vision, 630

  • BIP!
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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    172
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
172
Top 1%
Top 1%
Top 10%
Green