Crop and weed classification based on a colour and NIR sensory setup

Kodagoda, S; Zhang, Z; Dissanayake, G

Crop and weed classification based on a colour and NIR sensory setup

Kodagoda, S

Zhang, Z Dissanayake, G

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Publisher:: Whittles Publishing
Publication Type:: Conference Proceeding
Citation:: Proc. Innovative Production Machines and Systems - 5th I*PROMS Virtual International Conference, 2010, pp. 212 - 217
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.editor	Pham, DT	en_US
dc.contributor.editor	Eldukhri, EE	en_US
dc.contributor.editor	Soroka, AJ	en_US
dc.date	2009-07-06	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Proc. Innovative Production Machines and Systems - 5th I*PROMS Virtual International Conference, 2010, pp. 212 - 217	en_US
dc.identifier.isbn	978-184995-006-0	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16408
dc.description.abstract	This paper presents a multi-modal sensing approach for detecting Bidens pilosa L (commonly known as cobbler's peg) in wheat farms. Bidens is an annual broad leaf weed in tropical and sub-tropical regions and reported to be a weed that needs to be identified and eliminated when farming thirty one different crop varieties. Both Bidens and wheat leaves can have similar visual cues due to the curled up nature of the wheat leaves. This makes a straightforward visual image (RGB) based classification nontrivial. Therefore, we have integrated another informative band in the spectrum, which is the NIR band. Information retrieved is processed to generate a series of cues that are then fed into a classification algorithm. Bidens and wheat plant species are used to verify the classification algorithm. The proposed technique is able to achieve an accuracy of 88% - 95% even when there is substantial overlapping between Bidens and wheat leaves.	en_US
dc.publisher	Whittles Publishing	en_US
dc.relation.ispartof	Proc. Innovative Production Machines and Systems - 5th I*PROMS Virtual International Conference	en_US
dc.relation.ispartof	International Conference on Innovative Production Machines and Systems	en_US
dc.title	Crop and weed classification based on a colour and NIR sensory setup	en_US
dc.type	Conference Proceeding
utslib.location	Cardiff, UK	en_US
utslib.location.activity	Virtual conference	en_US
utslib.for	0914 Resources Engineering and Extractive Metallurgy	en_US
dc.location.activity	Virtual conference	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Cardiff, UK	en_US
pubs.start-date	2009-07-06	en_US

Abstract:

This paper presents a multi-modal sensing approach for detecting Bidens pilosa L (commonly known as cobbler's peg) in wheat farms. Bidens is an annual broad leaf weed in tropical and sub-tropical regions and reported to be a weed that needs to be identified and eliminated when farming thirty one different crop varieties. Both Bidens and wheat leaves can have similar visual cues due to the curled up nature of the wheat leaves. This makes a straightforward visual image (RGB) based classification nontrivial. Therefore, we have integrated another informative band in the spectrum, which is the NIR band. Information retrieved is processed to generate a series of cues that are then fed into a classification algorithm. Bidens and wheat plant species are used to verify the classification algorithm. The proposed technique is able to achieve an accuracy of 88% - 95% even when there is substantial overlapping between Bidens and wheat leaves.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16408