New Zealand Journal of Agricultural Research abstracts
Field pest identification by an improved Gabor texture segmentation
scheme
Zhao Juan*
Cheng Xiao-Ping
Faculty of Computer and Information Science
Southwest University
Chongqing 400715
China
*Author for correspondence: anysun@swu.edu.cn
Abstract Segmentation of pests is a critical step in using
machine vision for field automation tasks. A new method called
GaborBoostSVM is proposed in this paper. The method comprises Gabor
wavelets-based feature extraction, AdaBoost-based feature selection and
SVM-based pattern recognition algorithm. It performed unsupervised
classification in field images into pest and background categories for
real-time selective insecticide application. The results showed that
the method is capable of performing texture-based pest and background
classification consistently high, effectively and with high
classification accuracy.
Keywords AdaBoost; field pests; Gabor filter; image
segmentation; SVM
A07083; Online publication date 21 December 2007; Received and accepted
10 August 2007
New Zealand Journal of Agricultural Research, 2007, Vol. 50:
719–723
0028–8233/07/5005–0719 © The Royal Society of New Zealand
2007
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