Title: Classification of Bidens in wheat farms

Authors: Zhengzhi Zhang, Sarath Kodagoda, David Ruiz, Jayantha Katupitiya, Gamini Dissanayake

Addresses: ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, Australia. ' ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, Australia. ' Australian Centre for Precision Agriculture (ACPA), University of Sydney, Sydney, Australia. ' ARC Centre of Excellence for Autonomous Systems (CAS), University of New South Wales, Australia. ' ARC Centre of Excellence for Autonomous Systems (CAS), University of Technology, Sydney, Australia

Abstract: Bidens pilosa L. (commonly known as cobbler|s peg) is an annual broad leaf weed widely distributed in tropical and subtropical regions of the world and is reported to be a weed of 31 crops, including wheat. Automatic detection of Bidens in wheat farms is a non-trivial problem due to their similarity in colour and presence of occlusions. This paper proposes a methodology which could be used to discriminate Bidens from wheat to be used in operations such as autonomous weed destruction. A spectrometer is used to analyse the optical properties of Bidens and wheat leaves while achieving high classification results. However, due to the practical constraints of using spectrometers, a colour camera-based technique is proposed. It is shown that the colour-based segmentation followed by shape-based validation algorithm gives rise to high detection rates with lower false detections. We have experimentally evaluated the algorithm with Bidens detection rate of 80% and a false alarm rate of 10%.

Keywords: weed detection; classification; sensing; precision agriculture; Bidens pilosa L.; cobbler|s peg; wheat farms; automatic detection; colour-based segmentation; shape-based validation.

DOI: 10.1504/IJCAT.2010.034740

International Journal of Computer Applications in Technology, 2010 Vol.39 No.1/2/3, pp.123 - 129

Published online: 18 Aug 2010 *

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