Identification of cultivated rice MR 263 seed and weedy rice seed variants using CCD camera based-machine vision system

• Main Author: Aimi Athirah, Aznan
• Other Authors: Professor Ir. Dr. Ibni Hajar Bin Hj. Rukunudin
• Format: Thesis
• Language: English
• Published: Universiti Malaysia Perlis (UniMAP) 2019
• Subjects: Rice seed; Rice seed industry > Malaysia; MR 263 > Rice variety; Paddy production industry > Malaysia
• Online Access: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61514
• Institution: Universiti Malaysia Perlis

The main purpose of this study was to develop rice seed identification research prototype system to classify cultivated rice and weedy rice seeds variants using machine vision system through the extraction of morphological, colour, and textural features of the seeds. Five different types of weedy rice seeds variants samples of open panicle, close panicle and awn type were collected from several commercial farms in Kedah. The MR 263 seed was obtained from a commercial rice seed bag from a local supplier. In this study, seed samples were consisted of 600 seeds of MR 263 and 600 seeds from weedy rice seed variants group. Images of the rice seed samples were acquired using a charge coupled device (CCD) colour camera. Laboratory Virtual Instrument Engineering Workbench (LabVIEW) development environment was used to program the image processing, features extraction and the classification analysis. There was 12 morphological, 6 colour and 5 textural features were extracted from the seed images. Four types of classification model namely morphology, colour, texture and morphologycolour- texture models were established based on the extracted data. Each of the models was analyzed for feature selection using stepwise discriminant analysis (SDA) to develop the optimized features model. Then, the original and optimized features models were analyzed using 3 classifiers; discriminant function analysis (DFA), support vector machine (SVM) and neural network (NN). Analysis of variance (ANOVA) was conducted on the 3 classifiers to evaluate the mean classification accuracy levels of the 8 extracted features models developed. The ANOVA showed that there is no significant difference of mean classification accuracies between the 3 classifiers. The classification results using morphology-colour-texture features model was found to obtain higher classification accuracy levels as compared to the single feature models. An identification system was developed in the LabVIEW to classify the cultivated rice MR 263 and weedy rice seed groups using optimized features of the morphology-colourtexture model in DFA. The developed system was able to classify both seed groups at 99.4% accuracy level using testing data set.