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Journal of Information Science and Engineering, Vol. 30 No. 4, pp. 1227-1244 (July 2014)


Weed Image Classification using Wavelet Transform, Stepwise Linear Discriminant Analysis, and Support Vector Machines for an Automatic Spray Control System*


MUHAMMAD HAMEED SIDDIQI1, SEOK-WON LEE2 AND ADIL MEHMOOD KHAN2,+
1Department of Computer Engineering
Kyung Hee University
Suwon, 446-701 Korea
2Division of Information and Computer Engineering
Ajou University
Gyeonggi-do, 442-749 Korea
E-mail: siddiqi@oslab.khu.ac.kr; {leesw; amtareen}@ajou.ac.kr

We tested and validated the accuracy of wavelet transform along with stepwise linear discriminant analysis (SWLDA) and support vector machines (SVMs) for crop/weed classification for real time selective herbicides systems. Unlike previous systems, the proposed algorithm involves a pre-processing step, which helps to eliminate lighting effects to ensure high accuracy in real-life scenarios. We tested a large group of wavelets (46) and decomposed them up to four levels to classify weed images into weeds with broad leaves versus weeds with narrow leaves classes. SWLDA was then employed to reduce the feature space by extracting only the most meaningful features. Finally, the features provided by SWLDA were fed to the SVMs for classification. The proposed method was tested on a database of 1200 samples, which is a much larger database size than that studied previously (200-400 samples). Using confusion matrices, the crop/ weed classification results obtained using different wavelets at different decomposition levels were compared, and this approach was also compared with existing techniques that use statistical and structural approaches. The overall classification accuracy obtained using the symlet wavelet family was 98.1%. These results represent an improvement of 14% in performance compared with existing techniques.

Keywords: weed classification, global histogram equalization, wavelet transform, SWLDA, machine vision

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Received March 28, 2013; revised July 8 & August 27, 2013; accepted September 9, 2013.
Communicated by Chia-Feng Juang.
* This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No.2010-0028631).
+ Corresponding author: amtareen@ajou.ac.kr.