Robot for weed species plant‐specific management

Owen Bawden(Queensland University of Technology), Jason Kulk(Queensland University of Technology), R. Russell(Queensland University of Technology), Chris McCool(Queensland University of Technology), Andrew English(Queensland University of Technology), Feras Dayoub(Queensland University of Technology), Chris Lehnert(Queensland University of Technology), Tristán Pérez(Queensland University of Technology)
Journal of Field Robotics
June 6, 2017
Cited by 199Open Access
Full Text

Abstract

Abstract The 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.


Related Papers

No related papers found

Powered by citation graph analysis