Characterisation of Weedy Rice Seeds using Principal Component Analysis

Abstract

The weedy rice contamination in certified rice seed samples greatly impacts the Malaysian rice seed industry. The existing manual process to identify the weedy rice seed is only based on the physical appearance of the seed. The physical characteristics such as morphology, colour and texture image of the seeds were captured and analysed using image processing and the application of machine vision to understand the physical characteristics of the weedy rice seed. The objective of this study is to understand the physical characteristics of the weedy rice seed using Principal Component Analysis (PCA) transformation. A total of 7350 images of cultivated rice seeds from five major varieties and 895 images of weedy rice seeds were acquired using machine vision setup, and 67 features from the three major parameters (morphology, colour, and texture) were extracted. The test of equality of means based on Wilks' Lambda was performed to assess significant differences among the group parameters. PCA transformed data into principal components. The relationships between weedy rice seed and cultivated rice seed samples were examined through the score and loading plots of the PCA analysis. This newly transformed data visualises the experimental data's underlying structure and helps identify the parameters distinguishing between weedy rice and cultivated rice seed. The results on the PCA score plot have shown overlapping areas between the cultivated and weedy rice seeds, indicating high similarities between the seed samples.