Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine

• Published in: Journal of sensors 2020, Vol.2020 (2020), p.1-14
• Main Authors: Prasertsak, Anchalee, Chaitavon, Kosom, Prempree, Panintorn, Sinthupinyo, Wasin, Temniranrat, Pitchayagan, Kiratiratanapruk, Kantip, Porntheeraphat, Supanit
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; Deep learning; Economic development; Experiments; Feature extraction; Grading machines; Grain; Machine learning; Machine vision; Quality; Rice; Screening; Seeds; Statistical methods; Subgroups; Vision systems
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2020/7041310

To increase productivity in agricultural production, speed, and accuracy is the key requirement for long-term economic growth, competitiveness, and sustainability. Traditional manual paddy rice seed classification operations are costly and unreliable because human decisions in identifying objects and issues are inconsistent, subjective, and slow. Machine vision technology provides an alternative for automated processes, which are nondestructive, cost-effective, fast, and accurate techniques. In this work, we presented a study that utilized machine vision technology to classify 14 Oryza sativa rice varieties. Each cultivar used over 3,500 seed samples, a total of close to 50,000 seeds. There were three main processes, including preprocessing, feature extraction, and rice variety classification. We started the first process using a seed orientation method that aligned the seed bodies in the same direction. Next, a quality screening method was applied to detect unusual physical seed samples. Their physical information including shape, color, and texture properties was extracted to be data representations for the classification. Four methods (LR, LDA, k-NN, and SVM) of statistical machine learning techniques and five pretrained models (VGG16, VGG19, Xception, InceptionV3, and InceptionResNetV2) on deep learning techniques were applied for the classification performance comparison. In our study, the rice dataset were classified in both subgroups and collective groups for studying ambiguous relationships among them. The best accuracy was obtained from the SVM method at 90.61%, 82.71%, and 83.9% in subgroups 1 and 2 and the collective group, respectively, while the best accuracy on the deep learning techniques was at 95.15% from InceptionResNetV2 models. In addition, we showed an improvement in the overall performance of the system in terms of data qualities involving seed orientation and quality screening. Our study demonstrated a practical design of rice classification using machine vision technology.