Robot for weed species plant‐specific management. Issue 6 (6th June 2017)

• Record Type: Journal Article
• Authors: Bawden, Owen; Kulk, Jason; Russell, Ray; McCool, Chris; English, Andrew; Dayoub, Feras; Lehnert, Chris; Perez, Tristan
• Other Names: Ball David guestEditor.; Upcroft Ben guestEditor.; van Henten Eldert guestEditor.; van den Hengel Anton guestEditor.; Tokekar Pratap guestEditor.; Das Jnaneshwar guestEditor.
• Is Part Of: Journal of field robotics. Volume 34:Issue 6(2017)
• Page Start: 1179
• Page End: 1199
• Publication Date: 2017-06-06
• Subjects: Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21727
• Languages: English
• ISSNs: 1556-4959

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.