Title :
Classification of Bidens in Wheat Farms
Author :
Zhang, Zhenhao ; Kodagoda, Sarath ; Ruiz, Daniel ; Katupitiya, Jayantha ; Dissanayake, Gamini
Author_Institution :
ARC Centre of Excellence for Autonomous Syst. (CAS), Univ. of Technol., Sydney, NSW
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 nontrivial problem due to their similarity in color 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 analyze the optical properties of Bidens and wheat leaves while achieving high classification results. However, due to the practical constraints of using spectrometers, a color camera based technique is proposed. It is shown that the color 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 10% false alarm rate.
Keywords :
crops; image colour analysis; image segmentation; image sensors; object detection; pattern classification; Bidens pilosa L classification; color based segmentation; color camera based technique; wheat farms; Australia; Cameras; Content addressable storage; Costs; Crops; Linear discriminant analysis; Machine vision; Reflectivity; Shape; Spectroscopy;
Conference_Titel :
Mechatronics and Machine Vision in Practice, 2008. M2VIP 2008. 15th International Conference on
Conference_Location :
Auckland
Print_ISBN :
978-1-4244-3779-5
Electronic_ISBN :
978-0-473-13532-4
DOI :
10.1109/MMVIP.2008.4749584